CN102075136B - Soft measurement method for magnetic flux linkage of bearingless permanent magnet synchronous motor - Google Patents
Soft measurement method for magnetic flux linkage of bearingless permanent magnet synchronous motor Download PDFInfo
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- CN102075136B CN102075136B CN2011100038823A CN201110003882A CN102075136B CN 102075136 B CN102075136 B CN 102075136B CN 2011100038823 A CN2011100038823 A CN 2011100038823A CN 201110003882 A CN201110003882 A CN 201110003882A CN 102075136 B CN102075136 B CN 102075136B
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Abstract
The invention discloses a weighted least square support vector machine-based soft measurement method for magnetic flux linkage of a bearingless permanent magnet synchronous motor. The method comprises the following steps of: selecting a rotor position angle, torque winding current, levitation force winding current and rotor eccentric displacement of the bearingless permanent magnet synchronous motor as four input variables of a bearingless permanent magnet synchronous motor magnetic flux linkage soft measurement model, wherein the magnetic flux linkage y is taken as an input variable; acquiring representative input variable sample data and output variable sample data, performing normalization preprocessing on both the output variable and the input variables, and forming a modeling sample set for corresponding normalized values; performing residual analysis on the modeling sample set to acquire each sample weight; training the modeling sample set and establishing a bearingless permanent magnet synchronous motor magnetic flux linkage correlation model by using a weighted least square support vector machine; and finally abnormalizing an sy value to acquire the magnetic flux linkage y. Thus, the magnetic flux linkage value of the bearingless permanent magnet synchronous motor is predicted and controlled on line in real time.
Description
Technical field
The invention belongs to Electric Drive and information science crossing domain, a kind of based on Weighted Least Squares Support Vector Machines (Weighted Least Square Support Vector Machine, the flexible measurement method of bearing-free permanent magnet synchronous motor magnetic linkage WLS-SVM), create condition for the torque and the control of radial suspension force real-time online that realize bearing-free permanent magnet synchronous motor, be applicable to the high performance control of bearing-free permanent magnet synchronous motor.
Background technology
Bearing-free permanent magnet synchronous motor not only has the advantages such as the PMSM Servo System volume is little, lightweight, efficient is high, power factor is high, control characteristic is good, and have magnetic bearing without friction, without wear and tear, need not lubricate, the advantages such as high rotating speed and high accuracy, thereby it is with a wide range of applications at special dimensions such as Aero-Space, biological medicine, semiconductor manufacturings.
Bearing-free permanent magnet synchronous motor is in the air-gap field of the traditional permagnetic synchronous motor (magnetic field of the magnetic field of permanent magnet excitation and the excitation of torque winding, this two parts magnetic field is collectively referred to as " torque winding air-gap field ") the basis on, by the excitation of the suspending power winding in motor stator suspending power winding air-gap field, this two parts magnetic field superposes mutually, so it is very complicated that the motor gas-gap magnetic field space distributes, along with the Distribution of Magnetic Field in the rotor rotary electric machine is done cyclic variation, be in operation, different along with load and rotating speed, magnetic flux, electric current, torque, each amount of radial displacement all presents different variation waveforms.The inductance of winding and magnetic linkage all are nonlinear functions of rotor position angle, radial displacement and each winding current.The basis of realizing the normal suspension operation of bearing-free permanent magnet synchronous motor is to control in real time torque and rotor radial suspending power, and torque and radial suspension force all are according to the electromagnetic theory method of virtual displacement, winding magnetic energy is asked local derviation, and magnetic energy is to try to achieve by the inductance matrix of magnetic linkage and winding.The non-linear correlation model of therefore, trying to achieve magnetic linkage is the basis of bearing-free permanent magnet synchronous motor being carried out real-time online control.
At present existing a lot of documents have proposed the method for measurement of magnetic linkage: based on the direct method of measurement of search coil, based on stator voltage flux linkage model method, novel magnetic linkage integration method, EKF method etc.The direct method of measurement is subjected to noise jamming and parameter of electric machine error effect very large; Realize based on the mode of stator voltage flux linkage model method with integration, because the method utilizes pure integral operation to calculate magnetic linkage, even the therefore parameter entirely accurate of observation model, thereby small DC deviation still can produce larger cumulative errors and cause integral result skew even the saturated accuracy of observation that affects in the voltage, current signal; Novel magnetic linkage integration method is on the basis of low-pass first order filter the magnetic linkage of output to be drawn back and feeds back, set up the algorithm between a kind of pure integration and the low-pass filtering link, the control effect significantly but the algorithm more complicated, and the introducing of low pass filter makes the amplitude of magnetic linkage and phase place that variation all occur, and the impact when low-speed range is even more serious; The EKF method is used the minimum variance optimum prediction estimation technique to weaken random disturbances and is measured noise, the control precision when having improved to a certain extent low speed, but algorithm is complicated, and parameter configuration lacks certain standard, and operand is very large.
Soft-measuring technique has obtained a large amount of successful application in industry in recent years, has solved the many measurement problems that can not survey pass key control index.Soft-measuring technique is to set up model about the leading variable that can not survey (or being difficult to measure) by the auxiliary variable that can survey, thereby On-line Estimation goes out the quality index estimated value of real-time continuous.
Summary of the invention
For features such as the nonlinearity of magnetic linkage in the present bearing-free permanent magnet synchronous motor and difficult measurements, but measure in real time or measure in real time deficiency of the very high magnetic linkage variable method of measurement of cost and a kind of flexible measurement method of the bearing-free permanent magnet synchronous motor magnetic linkage based on Weighted Least Squares Support Vector Machines is provided with physical sensors is online in order to solve bearing-free permanent magnet synchronous motor stable suspersion extremely important be difficult to directly in service, need not to the bearing-free permanent magnet synchronous motor system make any change can realize magnetic linkage in real time, online, accurate PREDICTIVE CONTROL.
The technical solution used in the present invention is to adopt successively following steps: the rotor position angle of 1) choosing bearing-free permanent magnet synchronous motor
x 1, the torque winding current
x 2, the suspending power winding current
x 3, rotor eccentric displacement
x 4As 4 input variables of bearing-free permanent magnet synchronous motor magnetic linkage soft-sensing model, output variable is magnetic linkage
y2) gather representational input variable and output variable sample data, output variable and input variable all carried out the normalization preliminary treatment, to the analog value after the normalization [
Sx 1,
Sx 2,
Sx 3,
Sx 4,
Sy *] formation modeling sample collection; 3) the modeling sample collection is carried out residual analysis, obtain each sample weights
w j , based on sample weights
w j Training modeling sample collection utilizes Weighted Least Squares Support Vector Machines to set up bearing-free permanent magnet synchronous motor magnetic linkage correlation model
,
nIt is number of samples; The
jResidual error between individual sample data match value and the actual value
Sx =[
Sx 1,
Sx 2,
Sx 3,
Sx 4];
, be RBF radial basis kernel function; σ
2It is the width of RBF radial basis kernel function;
α j Be Lagrange multiplier,
j=1,2 ...,
n,
bIt is bias;
According to
Obtain
bWith
α j Value;
CPenalty factor,
y j It is the output variable of sample set data; Sample weights
w j Through type
Calculative determination, T are the robust size estimation values of residual error; 4) in line computation sy value, the sy value is carried out can obtaining after renormalization is processed the magnetic linkage of bearing-free permanent magnet synchronous motor
y
The invention has the advantages that:
1, the present invention is based on the WLS-SVM modeling method, realization is carried out self adaptation to each sample and is adjusted the weights size, utilize SVMs structural risk minimization, strong nonlinearity ability to express, set up model and have good generalization ability, by the analyzing samples residual error, each sample is carried out weights to be distributed, improve the model prediction performance, set up the non-linear correlation model of the bearing-free permanent magnet synchronous motor magnetic linkage with superperformance.
2, need not use experience knowledge, also need not to understand in depth the operation mechanism characteristic of control object bearing-free permanent magnet synchronous motor, only need to use the input and output data just can realize the Black-Box identification of non-linear object, identification process is simple, adjustable parameter is few, and pace of learning is fast.
3. required input signal is the local direct measurable variable that obtains easily in the Practical Project in the flexible measurement method of the present invention, the software programming that the WLS-SVM algorithm passes through itself realizes, do not need real-time online PREDICTIVE CONTROL that the bearing-free permanent magnet synchronous motor system is made any change and can realize magnetic linkage, the realization expense is low, safe and reliable, be easy to Project Realization.Realized the online real-time estimate control of magnetic linkage value of bearing-free permanent magnet synchronous motor, torque and the control of radial suspension force real-time online that realizes bearing-free permanent magnet synchronous motor has been had great importance.
Description of drawings
Below in conjunction with the drawings and specific embodiments the present invention is described in further detail:
Fig. 1 is flow chart of the present invention.
Embodiment
Referring to Fig. 1, at first determine input variable (independent variable) and the output variable (dependent variable) of bearing-free permanent magnet synchronous motor magnetic linkage soft-sensing model; Then modeling sample is carried out the normalization preliminary treatment, obtain the input and output value of same excursion; Then set up the correlation model based on the bearing-free permanent magnet synchronous motor magnetic linkage of Weighted Least Squares Support Vector Machines; By being carried out renormalization, the output valve of correlation model tries to achieve in real time online the model calculated value at last, i.e. the online calculated value of bearing-free permanent magnet synchronous motor magnetic linkage soft-sensing model.Concrete enforcement divides following 4 steps:
1, chooses input variable and output variable
When the bearing-free permanent magnet synchronous motor stable suspersion moves, different along with load and rotating speed, magnetic linkage will present different variation waveforms, be the nonlinear function of rotor position angle, radial displacement and each winding current, for this reason, choose rotor position angle
x 1, the torque winding current
x 2, the suspending power winding current
x 3, rotor eccentric displacement
x 44 input variables as bearing-free permanent magnet synchronous motor magnetic linkage soft-sensing model.Wherein, rotor position angle
x 1Directly measure acquisition by increment photoelectric code disk, the torque winding current
x 2With the suspending power winding current
x 3Directly measure acquisition, rotor eccentric displacement by Hall element
x 4Directly measure acquisition by eddy current displacement sensor, therefore, all input variables of bearing-free permanent magnet synchronous motor magnetic linkage soft-sensing model can be in real time, directly obtain online.The output variable of bearing-free permanent magnet synchronous motor magnetic linkage soft-sensing model is the magnetic linkage of bearing-free permanent magnet synchronous motor
y
2, the preliminary treatment of modeling sample
In actual applications, the initial data of directly measuring acquisition by relative photo code disc and physical sensors is not an order of magnitude, therefore, in order to eliminate the impact of dimension, 4 input variables that obtain is all needed to carry out the normalization preliminary treatment.4 input variables utilize formula (1) to carry out normalized:
In the formula (1),
i=1,2 ..., 4;
x i iThe actual measured value of individual input variable;
Sx i iValue after the individual input variable normalization; [
x i_ min
,
x i_ max
] be
iThe excursion of individual input variable; [
m 1,
m 2] be the excursion of input variable after the normalization.
The magnetic linkage of output variable bearing-free permanent magnet synchronous motor
yUtilize formula (2) to carry out normalized:
In the formula (2),
It is the value after the output variable normalization; [
y Min,
y Max] be the excursion of output variable; [
m 1,
m 2] be the excursion of output variable after the normalization.
Gather
nOrganize representational sample data, wherein every group of sample data comprises 4 input variables
x 1,
x 2,
x 3,
x 4With corresponding output variable
y, after process formula (1) and formula (2) normalization be [
Sx 1,
Sx 2,
Sx 3,
Sx 4,
], form the modeling sample collection.
3, foundation is based on the bearing-free permanent magnet synchronous motor magnetic linkage correlation model of Weighted Least Squares Support Vector Machines
By to by the sample size that obtains after formula (1) and formula (2) normalization being
nThe modeling sample collection carry out residual analysis,
jResidual error between individual sample data match value and the actual value
, utilize formula (3) can obtain the weights of each sample:
In the formula (3),
j=1,2 ...,
n w j jThe weights of individual sample data;
ξ j jResidual error between individual sample data match value and the actual value; T represents the robust size estimation value of residual error, can utilize formula (4) to calculate:
In the formula (4), function sort represents residual values is carried out from lower to large arrangement; Median is got in function median representative; Function mean representative is averaged.
Based on each sample data weights
w j With training modeling sample collection, utilize the Weighted Least Squares Support Vector Machines technology to set up the model in space.According to supporting vector machine model, utilize formula (5) to obtain
bWith
α j Value:
In the formula (5),
, be RBF radial basis kernel function; σ
2It is the width of RBF radial basis kernel function;
α j 〉=0, be Lagrange multiplier;
CIt is penalty factor;
bIt is bias;
y j It is the output variable of sample set data.
By the training data interative computation, obtain being calculated by formula (5)
bWith
α j Value, the substitution supporting vector machine model, namely
, obtain bearing-free permanent magnet synchronous motor magnetic linkage correlation model, be made as:
Order
Sx =[
Sx 1,
Sx 2,
Sx 3,
Sx 4], then
（7）
In the formula (7),
fFor
Sx With
SyBetween supporting vector machine model.
4, at line computation bearing-free permanent magnet synchronous motor magnetic linkage soft-sensing model
The result that formula (7) is calculated
SyCarry out renormalization and process, just can be in the hope of the model calculated value of bearing-free permanent magnet synchronous motor magnetic linkage, this calculated value namely is the magnetic linkage of bearing-free permanent magnet synchronous motor
y, namely
To sum up, based on the rotor position angle of bearing-free permanent magnet synchronous motor
x 1, the torque winding current
x 2, the suspending power winding current
x 3And rotor eccentric displacement
x 4, the in real time directly measured value through type (1) of these 4 input variables try to achieve [
x 1,
x 2,
x 3,
x 4] must be worth after the normalization
Sx =[
Sx 1,
Sx 2,
Sx 3,
Sx 4]; Through type (7) is tried to achieve the model output valve
SyThrough type (8) renormalization can be tried to achieve the calculated value of flux linkage model
y, i.e. the online calculated value of bearing-free permanent magnet synchronous motor magnetic linkage soft-sensing model.
According to the above, just can realize the present invention.Other changes and modifications to those skilled in the art makes in the case of without departing from the spirit and scope of protection of the present invention still are included within the protection range of the present invention.
Claims (1)
1. the flexible measurement method of a bearing-free permanent magnet synchronous motor magnetic linkage is characterized in that adopting successively following steps:
1) chooses the rotor position angle of bearing-free permanent magnet synchronous motor
x 1, the torque winding current
x 2, the suspending power winding current
x 3, rotor eccentric displacement
x 4As 4 input variables of bearing-free permanent magnet synchronous motor magnetic linkage soft-sensing model, output variable is magnetic linkage
y
2) gather representational input variable and output variable sample data, output variable and input variable all carried out the normalization preliminary treatment, to the analog value after the normalization [
Sx 1,
Sx 2,
Sx 3,
Sx 4,
Sy *] forming the modeling sample collection, the output variable sample data is through the pretreated value of normalization
Sy *Be the sample data match value;
3) the modeling sample collection is carried out residual analysis, obtain each sample weights
w j , based on sample weights
w j Training modeling sample collection utilizes Weighted Least Squares Support Vector Machines to set up bearing-free permanent magnet synchronous motor magnetic linkage correlation model
,
nIt is number of samples; The
jResidual error between individual sample data match value and the correlation model
Sx =[
Sx 1,
Sx 2,
Sx 3,
Sx 4];
According to
Obtain
bWith
α j Value;
CPenalty factor,
y j It is the output variable of sample set data;
Sample weights
w j Through type
Calculative determination, T are the robust size estimation values of residual error;
4) in line computation sy value, the sy value is carried out can obtaining after renormalization is processed the magnetic linkage of bearing-free permanent magnet synchronous motor
y
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| CN2011100038823A CN102075136B (en) | 2011-01-10 | 2011-01-10 | Soft measurement method for magnetic flux linkage of bearingless permanent magnet synchronous motor |
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| CN102075136B true CN102075136B (en) | 2013-04-17 |
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Families Citing this family (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102510253B (en) * | 2011-11-07 | 2014-06-25 | 江苏大学 | Sensorless controller of bearingless synchronous reluctance motor and method for controlling sensorless controller |
| CN102831301B (en) * | 2012-08-06 | 2016-04-06 | 江苏大学 | A kind of modeling method of soft measuring instrument of induction-type bearingless motor magnetic linkage |
| CN103259486B (en) * | 2013-05-07 | 2015-08-12 | 上海大学 | Based on the model prediction three level Direct Torque Control of state trajectory extrapolation |
| CN105915141B (en) * | 2016-05-09 | 2018-09-21 | 中国第一汽车股份有限公司 | Permanent-magnetism synchronous motor permanent magnetic body magnetic linkage on-line measurement system and measurement method |
| CN106130429B (en) * | 2016-08-22 | 2019-03-05 | 江苏大学 | Bearing-free permanent magnet synchronous motor predictive controller and building method |
| CN106712627B (en) * | 2017-01-22 | 2019-05-14 | 北京新能源汽车股份有限公司 | Method and device for acquiring key parameters of permanent magnet synchronous motor and electric vehicle |
| JPWO2018159104A1 (en) * | 2017-03-03 | 2019-12-26 | 日本電産株式会社 | Motor control method, motor control system, and electric power steering system |
| CN110224649B (en) * | 2019-07-03 | 2021-04-06 | 长安大学 | Method for DTC prediction control based on support vector machine |
| CN110988674A (en) * | 2019-11-19 | 2020-04-10 | 中南大学 | Health state monitoring method and system of permanent magnet synchronous motor and mobile terminal |
| JP7209877B2 (en) * | 2020-02-06 | 2023-01-20 | 三菱電機株式会社 | Angle detector |
| CN113037165B (en) * | 2021-03-12 | 2023-01-03 | 上海金脉电子科技有限公司 | Method and device for correcting flux linkage coefficient of permanent magnet synchronous motor |
| CN113992087B (en) * | 2021-11-05 | 2023-11-07 | 南京航空航天大学 | Full-speed-domain sensorless position estimation and control method and system for motor |
| CN115395863B (en) * | 2022-10-28 | 2023-01-31 | 南京工程学院 | Active magnetic bearing control method based on hybrid system theory |
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| CN1713490A (en) * | 2005-05-18 | 2005-12-28 | 江苏大学 | Digital-control servo system and its control for permanent magnet synchronous motor without bearing |
| CN101425775A (en) * | 2008-12-02 | 2009-05-06 | 江苏大学 | Controller and controlling method for non-bearing permanent magnet synchronous electric motor |
| CN201374678Y (en) * | 2009-02-11 | 2009-12-30 | 江苏大学 | Controller of bearing-free permanent magnetic synchronous motor |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1713490A (en) * | 2005-05-18 | 2005-12-28 | 江苏大学 | Digital-control servo system and its control for permanent magnet synchronous motor without bearing |
| CN101425775A (en) * | 2008-12-02 | 2009-05-06 | 江苏大学 | Controller and controlling method for non-bearing permanent magnet synchronous electric motor |
| CN201374678Y (en) * | 2009-02-11 | 2009-12-30 | 江苏大学 | Controller of bearing-free permanent magnetic synchronous motor |
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