CN108390605A - Permanent magnet synchronous motor method for estimating state based on fuzzy variable structure model reference adaptive observer - Google Patents
Permanent magnet synchronous motor method for estimating state based on fuzzy variable structure model reference adaptive observer Download PDFInfo
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- CN108390605A CN108390605A CN201810256794.6A CN201810256794A CN108390605A CN 108390605 A CN108390605 A CN 108390605A CN 201810256794 A CN201810256794 A CN 201810256794A CN 108390605 A CN108390605 A CN 108390605A
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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
- H02P21/0003—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control
- H02P21/001—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control using fuzzy control
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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
- H02P21/0003—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
- H02P21/13—Observer control, e.g. using Luenberger observers or Kalman filters
-
- 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
- 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
- H02P21/18—Estimation of position or speed
-
- 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
- H02P21/24—Vector control not involving the use of rotor position or rotor speed sensors
- H02P21/28—Stator flux based control
-
- 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
- H02P25/03—Synchronous motors with brushless excitation
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- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Automation & Control Theory (AREA)
- Fuzzy Systems (AREA)
- Control Of Electric Motors In General (AREA)
- Control Of Ac Motors In General (AREA)
Abstract
The invention discloses the permanent magnet synchronous motor method for estimating state based on fuzzy variable structure model reference adaptive observer, according to the stator voltage and stator current detected in real time, constructs a kind of New model reference adaptive observer and rotating speed is estimated.Such Type New Observer is characterized in that the PI correction links by pure integrator in conventional model reference adaptive observer being improved and being introduced stator flux observer error, solves direct current biasing and error accumulation problem caused by pure integral element in observer.Meanwhile Rotating speed measring problem cannot be realized in larger velocity interval for system, devise a kind of fuzzy variable structure adaptive law.Even if the permanent magnet synchronous motor method for estimating state of the present invention still has preferable estimation performance there are direct current biasing and the accumulation of error, the dynamic tracing performance and estimated accuracy to rotor-position and speed are improved.
Description
Technical field
The invention belongs to permanent magnet synchronous motor position Sensorless Control fields, more particularly to one kind being based on fuzzy variable structure
The permanent magnet synchronous motor method for estimating state of model reference adaptive observer.
Background technology
In recent decades, it is excellent that permasyn morot is small with its, efficient, power density is big, dynamic property is good etc.
Point has been widely used in high performance servo system.For the vector controlled of permanent magnet synchronous motor, it is accurate to obtain
Position and speed information it is very crucial.The position and speed of rotor can be by installing mechanical pick-up device example on rotor shaft
It is detected such as photoelectric encoder, but these sensors not only increase cost and reduce the reliability of system, limit simultaneously
The application range of permanent magnet synchronous motor.Therefore, research permanent magnet synchronous motor sensorless technology has important theory and reality
With value.
By domestic and foreign scholars' years of researches and summary, sensorless strategy, which has primarily formed, following several widely to be recognized
Can method：Using PMSM basic electromagnetic relationship estimated positions and rotating speed, this method calculates simply, and dynamic response is fast, however
It is especially sensitive to the parameter of electric machine, and estimation is inaccurate when motor speed is relatively low, it is impossible to be used in it is required that the occasion of motor positive and inverse；
PMSM rotor-positions based on observer and speed estimation method, but this algorithm is complicated, it is computationally intensive while certain
Method of estimation based on observer needs to use the statistical parameter of many random errors, and model is complicated, and it is more to be related to factor so that
The Comparision for analyzing these parameters is difficult；The method of high frequency injection, by injecting high frequency voltage to motor, and it is corresponding to detect it
Electric current obtain position and the rotating speed of rotor.It can also be obtained when this method can be applied to wider velocity interval and low speed although
To preferable estimation result, but the injection of high-frequency signal can bring high-frequency noise, and need special hardware circuit, only be applicable in
In built-in type motor；The method for estimating rotor-position and speed using magneto saliency.Utilize the voltage detected
And current value, the inductance at this position is calculated, and compared with the inductance value in the table pre-established, and then determine
Rotor-position.Due to being not easy to accurately measure stator voltage when transient state and low speed, the inductance value being calculated also has deviation.
Model reference adaptive method often calculates reference model and adjustable model using the flux linkage model of motor or current model
Difference had the advantages of simple structure and easy realization by pi regulator automatic adjusument motor speed, can pick out in motor very well
Motor rotor position when high speed and speed, but conventional model reference adaptive observer is in the pure integral element band of flux observation
Carry out deviation accumulation and DC offset problem has seriously affected estimation precision, such case is particularly evident in low speed.
Invention content
To solve the problems of conventional model reference adaptive observer, the present invention proposes that one kind can effectively solve the problem that
Deviation accumulation and DC offset problem present in conventional model reference adaptive observer are improved to permanent-magnetic synchronous motor rotor
The dynamic tracing performance of position and speed and the method for estimation of estimated accuracy.
To achieve the above object, the technical solution adopted in the present invention is seen based on fuzzy variable structure model reference adaptive
The permanent magnet synchronous motor method for estimating state of device is surveyed, this approach includes the following steps：
Step 1, New model reference adaptive observer is constructed, constructed New model reference adaptive is utilized to observe
Device carries out flux observation, including：
Detect the three-phase windings electric current i of motora、ib、ic, convert to obtain stator current in two-phase static coordinate by CLARK
The component i of lower α, β axis of systemα、iβ, according to stator current α, β axis component iα、iβ, point of the stator current estimated value in α, β axis
AmountAnd stator voltage is in the component u of α, β axisα、uβA kind of New model reference adaptive observer is constructed, institute is utilized
The New model reference adaptive observer of construction carries out flux observation, obtains systematic error e；
Step 2, processing of the systematic error e through fuzzy variable structure adaptive law link that flux observation obtains is extracted and is turned
Sub- positionAnd speed
Further, the method for construction New model reference adaptive observer is in the step 1：
Structure voltage model and current model and introduce high-pass filtering link, by the magnetic linkage value of the voltage model of acquisition with
The magnetic linkage value of current model is compared and an error amount obtaining, and error amount input pi regulator is carried out error correction,
It is fed into voltage model again.
Further, voltage equation of the construction permanent magnet synchronous motor under alpha-beta rest frame is：
Permanent magnet synchronous motor is under alpha-beta rest frame, the stator magnetic linkage equation based on voltage model：
Permanent magnet synchronous motor is under alpha-beta rest frame, the stator magnetic linkage equation based on current model：
Wherein, iα、iβRespectively α, β component of stator current, uα、uβRespectively α, β component of stator voltage, Lα、LβPoint
Not Wei inductance α, β component, Rs、LsRespectively stator resistance and inductance, ωrFor rotor velocity, θrFor rotor flux linkage vector with
The angle of stator voltage A phases, ψfFor stator magnetic linkage.
Further, in the step 2 using extracting rotor-position in fuzzy variable structure model reference adaptive observer
And speedSpecifically include following steps：
Step 2.1, it chooses magneto voltage model to be used as with reference to model, chooses magneto current model as adjustable
Model constructs a sliding-mode surface s using the system flux observation error e of the flux observation of two models output；
Step 2.2, Lyapunov functions are designedEnsure that sliding formwork stable operation need to makeObtain sliding formwork gain
For：
Wherein, Rs、LsIt is stator resistance and inductance, Kp、KiIt is proportion integral modulus, k respectivelysFor sliding formwork gain, ωrTo turn
Sub- angular rate, sgn (s) are sign function, and e is systematic error, and A, B are constant, and s is sliding-mode surface,For the derivative of s,For V
Derivative；
Step 2.3, k is adjusted by fuzzy control in real timesValue, so that its moment is met the value range of step 2.2.
Wherein：ε is the output quantity of fuzzy controller, ε >=0 and size is adjustable, when system reaches stable state e=0, is made
ε=0 is obtained, C is the output quantity of adjuster.
Further, sliding-mode surface building method is：Using integral sliding mode control,
Further, design of Fuzzy Controller：Select the change rate e of e and ecFor input variable, ε is exported by fuzzy control,
It is [0,10], input variable e and e to take ε domainscMembership function type be Gaussian, the membership function type of output variable ε is
Triangle.
Beneficial effects of the present invention：Permanent magnet synchronous motor method for estimating state proposed by the invention by model reference from
The PI correction links that pure integrator in observer was improved and introduced stator flux observer error are adapted to, solve observer
In direct current biasing and error accumulation problem caused by pure integral element.Meanwhile a kind of fuzzy variable structure adaptive law is devised,
It solves the problems, such as that system cannot realize Rotating speed measring in larger velocity interval, improves the dynamic to rotor-position and speed
Tracking performance and estimated accuracy.
Description of the drawings
Fig. 1 is the Speed Identification block diagram based on fuzzy variable structure model reference adaptive observer；
Fig. 2 is the vector controlled block diagram of permanent magnet synchronous motor；
Fig. 3 is fuzzy control rule；
Fig. 4 is the input/output relation figure of fuzzy control.
Specific implementation mode
In order to make the purpose , technical scheme and advantage of the present invention be clearer, with reference to the accompanying drawings and embodiments, right
The present invention is further elaborated.It should be appreciated that specific embodiment described herein is only used for explaining the present invention, not
For limiting the present invention.
As shown in Figure 1, 2, the Speed Identification block diagram based on fuzzy variable structure model reference adaptive observer, passes through：
Step 1, New model reference adaptive observer is constructed, constructed New model reference adaptive is utilized to observe
Device carries out flux observation, including：
Detect the three-phase windings electric current i of motora、ib、ic, convert to obtain stator current in two-phase static coordinate by CLARK
The component i of lower α, β axis of systemα、iβ, according to stator current α, β axis component iα、iβ, point of the stator current estimated value in α, β axis
AmountAnd stator voltage is in the component u of α, β axisα、uβConstruct a kind of New model reference adaptive observer, structure electricity
Pressing mold type and current model and high-pass filtering link is introduced, by the magnetic linkage value of the voltage model of acquisition and the magnetic linkage of current model
Value is compared and an error amount obtaining, error amount input pi regulator is carried out error correction, then be fed into voltage
Model carries out flux observation using the New model reference adaptive observer constructed, obtains systematic error e；
Step 2, processing of the systematic error e through fuzzy variable structure adaptive law link that flux observation obtains is extracted and is turned
Sub- positionAnd speed
Voltage equation of the permanent magnet synchronous motor under alpha-beta rest frame be：
Permanent magnet synchronous motor is under alpha-beta rest frame, the stator magnetic linkage equation based on voltage model：
Permanent magnet synchronous motor is under alpha-beta rest frame, the stator magnetic linkage equation based on current model：
θr=∫ ωrdt (4)
Wherein：iα、iβRespectively α, β component of stator current, uα、uβRespectively α, β component of stator voltage, Lα、LβPoint
Not Wei inductance α, β component, Rs、LsRespectively stator resistance and inductance, ωrFor rotor velocity, θrFor rotor flux linkage vector with
The angle of stator voltage A phases.
The present invention is in the model reference adaptive MRAS based on stator magnetic linkage, according to the mentality of designing of MRAS, by voltage
Model, which is used as, refers to model, using current model as adjustable model in parallel.
According to Popov hyperstable theories, rotor speed model, which can be obtained, is：
Wherein：Kp、KiFor the proportion integral modulus of pi regulator,Estimate speed, ψ for rotorα、ψβRespectively stator electricity
α, β component of sense.
It enablesThen rotor speed model simplification is：
Using integral sliding mode control, sliding-mode surface design is as follows：
Fuzzy adaptive controller is designed as：
Wherein：ksFor sliding formwork gain, sgn () is sign function.
Ensure that sliding formwork control is surely run, design Lyapunov functions are：
Ensure stable operation, need to make
Preset parameter sliding formwork control is replaced using Parameter-Varying Sliding Mode control method, k is adjusted in real time with fuzzy controlsValue, makes
Its moment is maintained at ideal value range, makes k using following methodsFinally it is stable at actual speed ωr：
Wherein：ε is the output quantity of fuzzy controller, ε >=0 and size is adjustable, when system reaches stable state e=0, is made
ε=0 is obtained, C is the output quantity of adjuster.
As shown in figure 3, design of Fuzzy Controller：Select the change rate e of e and ecIt is defeated by fuzzy control for input variable
Go out ε, it is [0,10], input variable e and e to take ε domainscMembership function type be Gaussian, the membership function class of output variable ε
Type is triangle；
The input/output relation that can obtain fuzzy control as shown in Figure 4, as systematic error e and its change rate ecIt is larger
When, system is in the speed governing starting stage, and the value of ε is bigger, and state variable is made quickly to tend to sliding-mode surface；As systematic error e and its
Change rate ecWhen smaller, system state variables reach sliding-mode surface, and the value of ε, which steadily glossily decreases up to, is intended to 0, effectively weaken
System chatter eventually arrives at stable state, this is consistent with actual conditions.
Above example is merely to illustrate the design philosophy and feature of the present invention, and its object is to make technology in the art
Personnel can understand the content of the present invention and implement it accordingly, and protection scope of the present invention is not limited to the above embodiments.So it is all according to
According to equivalent variations or modification made by disclosed principle, mentality of designing, within protection scope of the present invention.
Claims (8)
1. the permanent magnet synchronous motor method for estimating state based on fuzzy variable structure model reference adaptive observer, feature exist
In including the following steps：
Step 1, construct New model reference adaptive observer, utilize constructed New model reference adaptive observer into
Row flux observation, including：
Detect the three-phase windings electric current i of motora、ib、ic, convert to obtain stator current under two-phase stationary coordinate system by CLARK
The component i of α, β axisα、iβ, according to stator current α, β axis component iα、iβ, component of the stator current estimated value in α, β axisAnd stator voltage is in the component u of α, β axisα、uβA kind of New model reference adaptive observer is constructed, institute's structure is utilized
The New model reference adaptive observer made carries out flux observation, obtains systematic error e；
Step 2, rotor position is extracted to processing of the systematic error e through fuzzy variable structure adaptive law link that flux observation obtains
It setsAnd speed
2. the permanent magnet synchronous motor state according to claim 1 based on fuzzy variable structure model reference adaptive observer
Method of estimation, which is characterized in that the method for construction New model reference adaptive observer is in the step 1：
It builds voltage model and current model and introduces high-pass filtering link, by the magnetic linkage value and electric current of the voltage model of acquisition
The magnetic linkage value of model is compared and an error amount obtaining, error amount input pi regulator is carried out error correction, then instead
Feed enters voltage model.
3. the permanent magnet synchronous motor state according to claim 2 based on fuzzy variable structure model reference adaptive observer
Method of estimation, which is characterized in that constructing voltage equation of the permanent magnet synchronous motor under alpha-beta rest frame is：
Permanent magnet synchronous motor is under alpha-beta rest frame, the stator magnetic linkage equation based on voltage model：
Permanent magnet synchronous motor is under alpha-beta rest frame, the stator magnetic linkage equation based on current model：
Wherein, iα、iβRespectively α, β component of stator current, uα、uβRespectively α, β component of stator voltage, Lα、LβRespectively
α, β component of inductance, Rs、LsRespectively stator resistance and inductance, ωrFor rotor velocity, θrFor rotor flux linkage vector and stator
The angle of voltage A phases, ψfFor stator magnetic linkage.
4. the permanent magnet synchronous motor state according to claim 1 based on fuzzy variable structure model reference adaptive observer
Method of estimation, which is characterized in that using extracting rotor-position in fuzzy variable structure model reference adaptive observerAnd speed
DegreeSpecifically include following steps：
Step 2.1, it chooses magneto voltage model to be used as with reference to model, chooses magneto current model as adjustable die
Type constructs a sliding-mode surface s using the systematic error e of the flux observation of two models output；
Step 2.2, Lyapunov functions are designedEnsure that sliding formwork stable operation need to makeObtaining sliding formwork gain is：
Wherein, Rs、LsIt is stator resistance and inductance, Kp、KiIt is proportion integral modulus, k respectivelysFor sliding formwork gain, ωrFor rotor electricity
Angular speed, sgn (s) are sign function, and e is systematic error, and A, B are constant, and s is sliding-mode surface；
Step 2.3, k is adjusted by fuzzy control in real timesValue, so that its moment is met the value range of step 2.2.
Wherein：ε is the output quantity of fuzzy controller, ε >=0 and size is adjustable, when system reaches stable state e=0 so that ε
=0, C are the output quantities of adjuster.
5. the permanent magnet synchronous motor state according to claim 4 based on fuzzy variable structure model reference adaptive observer
Method of estimation, which is characterized in that the sliding-mode surface building method is：
Using integral sliding mode control,
6. the permanent magnet synchronous motor state according to claim 4 based on fuzzy variable structure model reference adaptive observer
Method of estimation, which is characterized in that described：
Wherein, s is sliding-mode surface,For the derivative of s,For the derivative of V.
7. the permanent magnet synchronous motor state according to claim 1 based on fuzzy variable structure model reference adaptive observer
Method of estimation, which is characterized in that design of Fuzzy Controller：Select the change rate e of e and ecFor input variable, pass through fuzzy control
ε is exported, the output quantity ε domains of modulus fuzzy controllers are [0,10].
8. the permanent magnet synchronous motor state according to claim 7 based on fuzzy variable structure model reference adaptive observer
Method of estimation, which is characterized in that input variable e and ecMembership function type be Gaussian, the membership function class of output variable ε
Type is triangle.
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| CN109782591A (en) * | 2018-12-26 | 2019-05-21 | 贵州电网有限责任公司 | A kind of SVC individual-phase control method based on fuzzy self-adaption sliding Mode Algorithm |
| CN110995089A (en) * | 2019-12-30 | 2020-04-10 | 江苏航迅飞信息科技有限公司 | Intelligent control method of multiphase permanent magnet fault-tolerant motor driving system |
| CN112234888A (en) * | 2020-09-29 | 2021-01-15 | 潍柴动力股份有限公司 | Current identification method for motor, motor controller and vehicle |
| CN112583316A (en) * | 2020-11-26 | 2021-03-30 | 江苏科技大学 | Control system and algorithm for inhibiting torque ripple of permanent magnet synchronous motor |
| 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 |
| CN113472262A (en) * | 2021-06-04 | 2021-10-01 | 江苏大学 | MTPA control method for identifying d-q axis inductance parameters of permanent magnet synchronous motor by adopting fuzzy logic control |
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| CN113972869A (en) * | 2021-10-29 | 2022-01-25 | 安徽工业大学 | PMSM (permanent magnet synchronous motor) position-sensorless vector control method based on novel sliding mode speed controller MRAS (maximum velocity estimation System) |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109782591A (en) * | 2018-12-26 | 2019-05-21 | 贵州电网有限责任公司 | A kind of SVC individual-phase control method based on fuzzy self-adaption sliding Mode Algorithm |
| CN110995089A (en) * | 2019-12-30 | 2020-04-10 | 江苏航迅飞信息科技有限公司 | Intelligent control method of multiphase permanent magnet fault-tolerant motor driving system |
| CN110995089B (en) * | 2019-12-30 | 2022-05-06 | 南京煜茂兮电子科技有限公司 | Intelligent control method of multiphase permanent magnet fault-tolerant motor driving system |
| CN112234888A (en) * | 2020-09-29 | 2021-01-15 | 潍柴动力股份有限公司 | Current identification method for motor, motor controller and vehicle |
| CN112234888B (en) * | 2020-09-29 | 2022-06-24 | 潍柴动力股份有限公司 | Current identification method for motor, motor controller and vehicle |
| CN112583316A (en) * | 2020-11-26 | 2021-03-30 | 江苏科技大学 | Control system and algorithm for inhibiting torque ripple of permanent magnet synchronous motor |
| 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 |
| CN113472262A (en) * | 2021-06-04 | 2021-10-01 | 江苏大学 | MTPA control method for identifying d-q axis inductance parameters of permanent magnet synchronous motor by adopting fuzzy logic control |
| CN113938073A (en) * | 2021-10-09 | 2022-01-14 | 广东汇天航空航天科技有限公司 | Motor position estimation method and device, controller, electric automobile and aerocar |
| CN113972869A (en) * | 2021-10-29 | 2022-01-25 | 安徽工业大学 | PMSM (permanent magnet synchronous motor) position-sensorless vector control method based on novel sliding mode speed controller MRAS (maximum velocity estimation System) |
| CN113972869B (en) * | 2021-10-29 | 2023-11-24 | 安徽工业大学 | PMSM (permanent magnet synchronous motor) position-free sensor vector control method based on novel sliding mode speed controller MRAS |
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