CN108832859A - A kind of predictive-current control method of the permanent-magnetism linear motor based on parameter identification - Google Patents
A kind of predictive-current control method of the permanent-magnetism linear motor based on parameter identification Download PDFInfo
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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
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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
- 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
- H02P25/06—Linear motors
- H02P25/064—Linear motors of the synchronous type
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Abstract
The invention discloses a kind of predictive-current control methods of permanent-magnetism linear motor based on parameter identification.Since the factors such as temperature, magnetic saturation will lead to the parameter of electric machine, motor control performance is impacted.The present invention will carry out on-line identification using inductance of the method for model reference adaptive system to linear motor, derive the parameter update law of motor inductances, the inductance parameters of identification are applied in real time in two vector prediction current controls, electric current steady-state error caused by parameter of electric machine deviation is eliminated, better control performance is obtained.
Description
Technical field
The present invention relates to linear permanent-magnet vernier motor field, specifically a kind of permanent magnetism vernier straight-line electric based on parameter identification
The predictive-current control of machine is conducive to the control performance for improving linear motor.
Background technique
With the development of urbanization, urban track traffic is undergoing the variation advanced by leaps and bounds, and consequent is motor
The rise of trailer system research.In recent years, Rail Transit System especially its driving motor the lighter, volume towards weight more
The mode development that small, speed is faster, operation is more stable, safety is more reliable.Therefore can stablize, reliably control meets this spy
The linear permanent-magnet vernier motor of point becomes the key for guaranteeing drive system reliability.
Permanent magnet synchronous motor parameters of electric machine such as resistance, inductance and magnetic linkage of required calling when controlling, although can be from motor
Nameplate or handbook in obtain, but motor factory data after longtime running can change, and may cause part motor
Parameter error is larger.Although by the available relevant initial parameter of off-line identification, because temperature rise and magnetic are full when work
With etc. factors will lead to parameter of electric machine real-time change again, so that actual parameter and off-line identification result is generated certain deviation.Therefore
It is necessary to the relevant parameters to permanent magnet synchronous motor to carry out on-line identification research.
On-line identification is that motor operates normally under control strategy control on one side, is distinguished in real time to motor relevant parameter on one side
Know.Its main feature is that requiring the whole operations for completing identification within a sampling period, constantly updated using collected new data
Identification result.On-line identification great advantage is that the process of identification does not influence the normal operation of motor, can obtain the true work of motor
Parameter when making, and generally also it is not required to external measuring circuit.It therefore can be according to the real-time parameter of motor using on-line identification
The parameter of controller is corrected in time, so that the electric system is reached better control performance.Common identification algorithm has frequency to ring
Ying Fa, model reference adaptive method, least square method, expanded Kalman filtration algorithm, intelligent algorithm etc., these algorithms exist
There are respective advantage and disadvantage in terms of parameter identification, needs deeper to be studied.
Summary of the invention
It is an object of the invention to obtain the relevant parameter of permanent-magnetism linear motor by identification algorithm, permanent-magnet linear electricity is eliminated
The side-termind effect of machine influences, and has better control to linear motor to reach.In order to more accurately obtain permanent-magnetism linear motor
Parameter, to reach better control, the present invention obtains the identification ginseng of permanent-magnetism linear motor by recursive model reference adaptive algorithm
Number.
For achieving the above object, the present invention adopts the following technical scheme that：
The predictive-current control method of permanent-magnetism linear motor based on parameter identification, including following steps：
Step 1：The mathematical model and permanent-magnetism linear motor discretization of permanent-magnetism linear motor under rotating coordinate system are derived first
Predictive control model；
Step 2：Secondly two vector prediction current Control Algorithms of prediction are derived, are applied in permanent-magnetism linear motor model, it is real
Now to two vector prediction current controls of permanent-magnetism linear motor；
Step 3：On-line identification is carried out to motor inductances and magnetic linkage using the method for model reference adaptive, derives motor electricity
The parameter update law of sense and magnetic linkage carries out on-line identification to the inductance parameters of linear motor, when identified parameters convergence, is distinguished
The inductance parameters of knowledge；
Step 4：The inductance parameters that identification algorithm is recognized are applied in two vector prediction current diffusion limited models
It goes, realizes the real-time update of the parameter of electric machine, by model reference adaptive identification algorithm and two vector prediction current Control Algorithm phases
In conjunction with realizing influences caused by closed-loop control, the elimination parameter of electric machine change, to reach the better controlling of permanent-magnetism linear motor
Energy.
Firstly, the predictive control model derivation of the mathematical model and discretization of permanent-magnetism linear motor is as follows：
Non-salient pole permanent magnet linear motor meets Ld=Lq=Ls, therefore electricity of the permanent-magnetism linear motor under dq rotating coordinate system
Pressure equation be：
In formula, ud,uqFor stator dq shaft voltage (V)；id,iqFor stator dq shaft voltage (A)；R is stator phase resistance (Ω)；
Ld,Lq,LsRespectively stator d axle inductance, stator q axle inductance, stator inductance (H)；ψfFor motor permanent magnet magnetic linkage (Wb)。ωeFor
The angular rate of motor.
Current of electric is selected as state variable, the state equation of permanent-magnetism linear motor can be expressed as：
Due to sampling period TsIt is sufficiently small, discretization can be carried out to current status equation using first order Taylor formula, i.e.,
It is approximately considered：
It is as follows by converting the permanent-magnetism linear motor predicted current model after capable of obtaining discretization：
In formula, coefficient matrix
Further, the basic principle of two vector prediction current Control Algorithms is as follows：
Classical forecast current control only acts on a voltage vector, two vector prediction current controls within a control period
It is in one nonzero voltage space vector of control period effects and a Zero voltage vector, and then when distributing the effect of two vectors
Between, realize better control performance.Due to the determination of Zero voltage vector, it is only necessary to an optimal nonzero voltage space vector is selected,
The selection gist of optimal nonzero voltage space vector is that the distance of distance reference voltage vector is minimum, is reference voltage vector in summary
Basic voltage vectors on the angular bisector of place sector are optimal nonzero voltage space vector.Reference voltage vector can be expressed as：
In formulaFor the reference value of stator current,For the stator current at k moment, LsFor stator inductance, TsFor sampling week
Phase, EkFor the counter electromotive force of motor；
Assuming that the nonzero voltage space vector selected is uopT, action time ti.Definition is according to reference voltage vector and non-zero
Error vector between voltage vector isAccording to mathematical relationship it is found that when Δ u is perpendicular to uoptWhen, Δ u has
Minimum value.By the available u of vector calculusopT andBetween angle theta1Meet following formula：
According to the definition of cosine：
In turn, the action time of nonzero voltage space vector can be expressed as：
Further, linear motor parameter update law design rule is as follows in step 3：
Motor status spatial model is：
In formula, ud,uqFor stator dq shaft voltage (V)；id,iqFor stator dq shaft voltage (A)；R is stator phase resistance (Ω)；
L is stator inductance (H)；ψfFor motor permanent magnet magnetic linkage (Wb)；ωeFor the angular rate of motor；
Enable 1/L=m, ψfAbove formula abbreviation is by/L=n：
Pi=Ai+Bu+C
In formula, current status vector i=[id iq]T, voltage status vector u=[ud uq]T, coefficient matrix
Adjustable model is：
In formula, current status vectorVoltage status vector u=[ud uq]T, coefficient matrix
For inductance L identifier, it is the identifier of magnetic linkage ψ, can be obtained：
In formula,It enablesThen have：
Pe=Ae-Iw
PMSM model reference adaptive parameter identification system is converted to the non-linear feedback system of standard, it is linear fixed to guarantee
Chang Qianxiang square Strict Positive Real, non-linear feedback loop meet Popov integral inequality, and Popov integral inequality is as follows：
η(t0-t1) it is integral function, in formula,For a limited normal number independent of t, w is nonlinear feedback side
Block output, v be Linear Time Invariant before to square export.
The design principle of parameter update law is to carry out on-line control by the parameter to adjustable model to make control system
Generalized error e be gradually intended to zero, in order to enable adjustment effect is still effective when generalized error e is zero, usually use ratio
The mode of integral is designed the adaptive law of parameter, according to Popov super-stable state law, to model reference adaptive control
System processed is overstable, not only to meet Linear Time Invariant forward path Strict Positive Real, but also non-linear feedback loop meets Popov product
Divide inequality, omits a series of derivation process, available motor inductances parameter identification relevant parameterAdaptive law be：
In formula,For the dq shaft current of identification, kp1,ki1It is the parameter value for needing to design, R is motor phase resistance, ud,
uqFor stator dq shaft voltage (V), vd,vqFor dq shaft current Identification Errors.
The beneficial effects of the invention are as follows：
1, the present invention obtains the identified parameters of linear motor by recursive model reference adaptive algorithm, and it is full to eliminate temperature, magnetic
With etc. factors lead to the changed influence of the parameter of electric machine, and predictive-current control is that a kind of control for relying on motor model is calculated
Method, obtaining the accurate parameter of electric machine will be greatly improved to control performance.
2, two vector prediction current controls can obtain preferable dynamic property and lesser current wave in motor control
It is dynamic, preferable control performance can be obtained applied to permanent-magnetism linear motor, but PREDICTIVE CONTROL is based on motor mathematical model
Control method has high requirement to the accuracy of the parameter of electric machine.Therefore the present invention swears recursive model reference adaptive algorithm and two
Amount predictive-current control combines, and the accurate parameter of electric machine can be applied in two vector prediction current controls, permanent-magnet linear
Motor control performance will obtain preferable improve.
3, a kind of permanent-magnetism linear motor predictive-current control algorithm based on parameter identification proposed by the invention, Ke Yiman
The requirement of sufficient field of urban rail high-precision operation, improves linear permanent-magnet vernier motor in the status of field of track traffic.
Detailed description of the invention
Fig. 1 is the survey current control block diagram of the permanent-magnetism linear motor based on parameter identification；
Fig. 2 is recursive model reference adaptive algorithm schematic diagram；
Fig. 3 is the parameter update law design frame chart of permanent-magnetism linear motor；
Fig. 4 is the motor inductances parameter value that identification obtains；
Fig. 5 is the predictive-current control three-phase current harmonic content for not using parameter identification；
Fig. 6 is the predictive-current control three-phase current harmonic content using parameter identification.
Specific implementation
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description.
The embodiment of the present invention is described below in detail, examples of the embodiments are shown in the accompanying drawings, wherein from beginning to end
Same or similar label indicates same or similar element or element with the same or similar functions.Below with reference to attached
The embodiment of figure description is exemplary, and for explaining only the invention, and is not considered as limiting the invention.
As shown in Fig. 1 structural block diagram, the present invention is the predictive-current control of the permanent-magnetism linear motor based on parameter identification, main
It to include recursive model reference adaptive algorithm and predictive-current control.Wherein the principle and parameter of recursive model reference adaptive algorithm are adaptive
It is as shown in Figure 2 and Figure 3 design principle should to be restrained.The parameter of electric machine that identification is obtained is applied in predictive-current control, to improve control
Performance.
The present invention carries out the predictive-current control based on parameter identification using permanent-magnetism linear motor as control object, to it,
Concrete measure is as follows：
1, the motor model of permanent-magnetism linear motor and the predicted current model of permanent-magnetism linear motor are derived first, are realized to straight
The predictive-current control of line motor.Before studying linear permanent-magnet vernier motor, first make the following assumptions：
(1) ignore magnetic circuit saturated phenomenon, it is believed that the inductance of each phase winding is constant.
(2) air gap be evenly distributed, air-gap reluctance it is constant.
(3) frequency variation and influence of the temperature change to winding resistance are not considered.
(4) rotor flux is in Sine distribution in air gap.
(5) control object of the invention belongs to Non-Salient-Pole Motor, Ld=Lq=280mH.
Since control object permanent-magnetism linear motor belongs to Non-Salient-Pole Motor, meet Ls=Ld=Lq, when motor is in stable state
Mathematical model be：
As can be seen that the major parameter of motor has three resistance, inductance, magnetic linkage parameters from the mathematical model of motor, by
Very little is influenced on Predictive Control System performance in electric motor resistance, can be ignored, present invention is generally directed to linear motor inductance
Carry out on-line identification.The structural block diagram of predictive-current control is as shown in Figure 1, the basic principle of predictive-current control is just seldom retouched
It states.
2, step 2：On-line identification is carried out to motor inductances and magnetic linkage using the method for model reference adaptive, derives motor
The parameter update law of inductance and magnetic linkage carries out on-line identification to the inductance parameters of linear motor, this is the portion of core of the present invention
Point, when identified parameters convergence, obtain the inductance parameters of identification.
Motor status spatial model is：
In formula, ud,uqFor stator dq shaft voltage (V)；id,iqFor stator dq shaft voltage (A)；R is stator phase resistance (Ω)；
L is stator inductance (H)；ψfFor motor permanent magnet magnetic linkage (Wb)
Enable 1/L=m, ψfAbove formula abbreviation is by/L=n：
Pi=Ai+Bu+C
In formula, current status vector i=[id iq]T, voltage status vector u=[ud uq]T, coefficient matrix
Adjustable model is：
In formula, current status vectorVoltage status vector u=[ud
uq]T, coefficient matrix
For electricity
Feel L identifier,For the identifier of magnetic linkage ψ, can be obtained：
In formula,
It enablesThen have：
Pe=Ae-Iw
PMSM model reference adaptive parameter identification system is converted to the non-linear feedback system of standard, it is linear fixed to guarantee
Chang Qianxiang square Strict Positive Real, non-linear feedback loop meet Popov integral inequality, and Popov integral inequality is as follows：
In formula,For a limited normal number independent of t, w is the output of nonlinear feedback square, and v is Linear Time Invariant
The output of forward direction square.
The design principle of parameter update law is to carry out on-line control by the parameter to adjustable model to make control system
Generalized error e be gradually intended to zero, in order to enable adjustment effect is still effective when generalized error e is zero, usually use ratio
The mode of integral is designed the adaptive law of parameter, according to Popov super-stable state law, to model reference adaptive control
System processed is overstable, not only to meet Linear Time Invariant forward path Strict Positive Real, but also non-linear feedback loop meets Popov product
Divide inequality, omits a series of derivation process, available motor inductances parameter identification relevant parameterAdaptive law be：
In formula,For the dq shaft current of identification, kp1,ki1It is the parameter value for needing to design, R is motor phase resistance, ud,
uqFor stator dq shaft voltage (V), vd,vqFor dq shaft current Identification Errors.
3, step 3：The inductance parameters that identification is obtained, are applied in the middle of predictive-current control model, realize motor ginseng
Several real-time updates eliminates the influence of parameter of electric machine variation, to reach better control performance.
The control system in such as Fig. 1 block diagram is constructed in Simulink, to the prediction electricity of the invention based on parameter identification
Stream method is verified.It feeds back as shown in figure 5, will not recognize the obtained parameter of electric machine before 0.2s to predictive-current control
It goes, motor inductances fluctuation is larger, and motor inductances converge on 0.28H substantially after 0.2s.From the point of view of control performance angle, such as Fig. 5, figure
Shown in 6, the three-phase current harmonic content of the predictive-current control of parameter identification is not used for 2.11%, using the pre- of parameter identification
The three-phase current harmonic content for surveying current control is 1.10%.Illustrate that the predictive-current control based on parameter identification can eliminate electricity
Machine Parameters variation bring influences, to improve control performance.
To sum up, the predictive-current control method of a kind of permanent-magnetism linear motor based on parameter identification proposed by the present invention, can
To be summarised as following steps：
Step 1：The mathematical model and permanent-magnetism linear motor discretization of permanent-magnetism linear motor under rotating coordinate system are derived first
Predictive control model；
Step 2：Secondly two vector prediction current Control Algorithms of prediction are derived, are applied in permanent-magnetism linear motor model, it is real
Now to two vector prediction current controls of permanent-magnetism linear motor；
Step 3：On-line identification is carried out to motor inductances and magnetic linkage using the method for model reference adaptive, derives motor electricity
The parameter update law of sense and magnetic linkage carries out on-line identification to the inductance parameters of linear motor, when identified parameters convergence, is distinguished
The inductance parameters of knowledge；
Step 4：The inductance parameters that identification algorithm is recognized are applied in two vector prediction current diffusion limited models
It goes, realizes the real-time update of the parameter of electric machine, by model reference adaptive identification algorithm and two vector prediction current Control Algorithm phases
In conjunction with realizing influences caused by closed-loop control, the elimination parameter of electric machine change, to reach the better controlling of permanent-magnetism linear motor
Energy.
In the description of this specification, reference term " one embodiment ", " some embodiments ", " illustrative examples ",
The description of " example ", " specific example " or " some examples " etc. means specific features described in conjunction with this embodiment or example, knot
Structure, material or feature are included at least one embodiment or example of the invention.In the present specification, to above-mentioned term
Schematic representation may not refer to the same embodiment or example.Moreover, specific features, structure, material or the spy of description
Point can be combined in any suitable manner in any one or more of the embodiments or examples.
Although an embodiment of the present invention has been shown and described, it will be understood by those skilled in the art that：Not
A variety of change, modification, replacement and modification can be carried out to these embodiments in the case where being detached from the principle of the present invention and objective, this
The range of invention is defined by the claims and their equivalents.
Claims (5)
1. the predictive-current control method of the permanent-magnetism linear motor based on parameter identification, which is characterized in that including following step
Suddenly：
Step 1：The mathematical model and permanent-magnetism linear motor discretization of permanent-magnetism linear motor is pre- first under derivation rotating coordinate system
Observing and controlling simulation；
Step 2：Secondly two vector prediction current Control Algorithms of prediction are derived, are applied in permanent-magnetism linear motor model, realization pair
Two vector prediction current controls of permanent-magnetism linear motor；
Step 3：On-line identification is carried out to motor inductances and magnetic linkage using the method for model reference adaptive, derive motor inductances and
The parameter update law of magnetic linkage carries out on-line identification to the inductance parameters of linear motor, when identified parameters convergence, obtains identification
Inductance parameters；
Step 4：The inductance parameters that identification algorithm is recognized are applied in the middle of two vector prediction current diffusion limited models, real
The real-time update of the existing parameter of electric machine, model reference adaptive identification algorithm and two vector prediction current Control Algorithms are combined,
Realize closed-loop control, eliminating influences caused by parameter of electric machine variation, to reach the better control performance of permanent-magnetism linear motor.
2. the permanent-magnetism linear motor predictive-current control method according to claim 1 based on parameter identification, feature exist
In the predictive control model derivation of the mathematical model and discretization of permanent-magnetism linear motor is as follows：
Step 1.1, non-salient pole permanent magnet linear motor meets Ld=Lq=Ls, therefore permanent-magnetism linear motor is under dq rotating coordinate system
Voltage equation be：
In formula, ud,uqFor stator dq shaft voltage (V)；id,iqFor stator dq shaft voltage (A)；R is stator phase resistance (Ω)；Ld,Lq,
LsRespectively stator d axle inductance, stator q axle inductance, stator inductance (H)；ψfFor motor permanent magnet magnetic linkage (Wb)；ωeIt is that permanent magnetism is straight
The angular rate of line motor；
Step 1.2, current of electric is selected as state variable, the state equation of permanent-magnetism linear motor can be expressed as：
Due to sampling period TsIt is sufficiently small, discretization can be carried out to current status equation using first order Taylor formula, i.e. approximation is recognized
For：
Step 1.3, as follows by converting the permanent-magnetism linear motor predicted current model after capable of obtaining discretization：
In formula, coefficient matrix
3. the permanent-magnetism linear motor predictive-current control method according to claim 2 based on parameter identification, feature exist
In two vector prediction current Control Algorithms are as follows：
Classical forecast current control only acts on a voltage vector within a control period, two vector prediction current controls be
One one nonzero voltage space vector of control period effects and a Zero voltage vector, and then the action time of two vectors is distributed,
Better control performance is realized, due to the determination of Zero voltage vector, it is only necessary to an optimal nonzero voltage space vector is selected, it is optimal
The selection gist of nonzero voltage space vector is that the distance of distance reference voltage vector is minimum, is reference voltage vector place in summary
Basic voltage vectors on the angular bisector of sector are optimal nonzero voltage space vector, and reference voltage vector can be expressed as：
In formulaFor the reference value of stator current,For the stator current at k moment, LsFor stator inductance, TsFor sampling period, Ek
For the counter electromotive force of motor；
Assuming that the nonzero voltage space vector selected is uopt, action time ti, define according to reference voltage vector and non-zero voltage
Error vector between vector isAccording to mathematical relationship it is found that when Δ u is perpendicular to uoptWhen, Δ u has most
Small value, by the available u of vector calculusoptWithBetween angle theta1Meet following formula：
According to the definition of cosine：
In turn, the action time of nonzero voltage space vector can be expressed as：
4. the permanent-magnetism linear motor predictive-current control method according to claim 1 based on parameter identification, feature exist
In the parameter update law of motor inductances and magnetic linkage is designed by certain rule, is adjusted the parameter of electric machine in adjustable model, is made
The parameter of adjustable model gradually approaches the actual parameter of motor；
Motor status spatial model is：
In formula, ud,uqFor stator dq shaft voltage (V)；id,iqFor stator dq shaft voltage (A)；R is stator phase resistance (Ω)；L is fixed
Sub- inductance (H)；ψfFor motor permanent magnet magnetic linkage (Wb)；ωeFor the angular rate of motor
Enable 1/L=m, ψfAbove formula abbreviation is by/L=n：
Pi=Ai+Bu+C
In formula, current status vector i=[id iq]T, voltage status vector u=[ud uq]T, coefficient matrix
Adjustable model is：
In formula, current status vectorVoltage status vector u=[ud uq]TCoefficient matrix：
For inductance L identifier,For the identifier of magnetic linkage ψ, can be obtained：
In formula,
It enablesThen have：
Pe=Ae-Iw
Permanent-magnetism linear motor model reference adaptive parameter identification system is converted to the non-linear feedback system of standard, guarantees line
The permanent forward direction square Strict Positive Real of property, non-linear feedback loop meet Popov integral inequality, and Popov integral inequality is as follows：
η(t0-t1) it is integral function, in formula,For a limited normal number independent of t, w is that nonlinear feedback square is defeated
Out, v be Linear Time Invariant before to square export.
5. the permanent-magnetism linear motor predictive-current control method according to claim 4 based on parameter identification, feature exist
In the design principle of parameter update law is to carry out the broad sense that on-line control makes control system by the parameter to adjustable model
Error e is gradually intended to zero, in order to enable adjustment effect is still effective when generalized error e is zero, usually using proportional integration
Mode is designed the adaptive law of parameter, according to Popov super-stable state law, to Model Reference Adaptive Control System
It is overstable, not only to meet Linear Time Invariant forward path Strict Positive Real, but also non-linear feedback loop meets Popov integral and differs
Formula omits a series of derivation process, available motor inductances parameter identification relevant parameterAdaptive law be：
In formula,For the dq shaft current of identification, kp1,ki1It is the parameter value for needing to design, R is motor phase resistance, ud,uqIt is fixed
Sub- dq shaft voltage (V), vd,vqFor dq shaft current Identification Errors.
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|---|---|---|---|---|
| CN109617468A (en) * | 2018-12-05 | 2019-04-12 | 长安大学 | Two Vector Modulation permasyn morot PREDICTIVE CONTROL optimization methods |
| CN110190795A (en) * | 2019-06-11 | 2019-08-30 | 东北大学 | A kind of permanent magnet synchronous motor tandem type Robust Prediction current control method |
| CN111404438A (en) * | 2020-02-25 | 2020-07-10 | 华中科技大学 | Method and system for tracking and controlling resonant frequency of linear oscillation motor |
| WO2020173064A1 (en) * | 2019-02-28 | 2020-09-03 | 华中科技大学 | Predictive thrust control method and system of arbitrary double vector model for linear induction motor |
| CN112054731A (en) * | 2020-08-19 | 2020-12-08 | 国电南瑞科技股份有限公司 | Permanent magnet synchronous motor parameter identification method based on model predictive control |
| CN112803854A (en) * | 2021-01-04 | 2021-05-14 | 北京理工大学 | Model reference self-adaption based permanent magnet synchronous motor parameter online identification method |
| CN112968644A (en) * | 2021-03-08 | 2021-06-15 | 上海交通大学 | Permanent magnet synchronous motor parameter online identification method, system, terminal and medium |
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2582036A2 (en) * | 2011-10-12 | 2013-04-17 | LSIS Co., Ltd. | Parameter estimating apparatus for permanent magnet synchronous motor driving system |
| CN104393814A (en) * | 2014-12-16 | 2015-03-04 | 山东大学 | Control method for permanent magnet synchronous motor |
| CN106357188A (en) * | 2016-10-20 | 2017-01-25 | 北方工业大学 | Unified single/double-vector model prediction control method and device for permanent magnet motors |
-
2018
- 2018-04-26 CN CN201810383792.3A patent/CN108832859A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2582036A2 (en) * | 2011-10-12 | 2013-04-17 | LSIS Co., Ltd. | Parameter estimating apparatus for permanent magnet synchronous motor driving system |
| CN104393814A (en) * | 2014-12-16 | 2015-03-04 | 山东大学 | Control method for permanent magnet synchronous motor |
| CN106357188A (en) * | 2016-10-20 | 2017-01-25 | 北方工业大学 | Unified single/double-vector model prediction control method and device for permanent magnet motors |
Non-Patent Citations (4)
| Title |
|---|
| YONGCHANG ZHANG ET AL.: "Performance Improvement of Model-Predictive Current Control of Permanent Magnet Synchronous Motor Drives", 《IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS》 * |
| 王明义等: "永磁同步直线电机电流预测控制优化设计", 《电机与控制学报》 * |
| 许强等: "永磁同步电机的自适应预测电流控制", 《电气传动》 * |
| 赵颖伟等: "基于 MRAS 的改进永磁同步电机参数辨识方法", 《微电机》 * |
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