CN110224649A - A method of DTC PREDICTIVE CONTROL is used for based on support vector machines - Google Patents
A method of DTC PREDICTIVE CONTROL is used for based on support vector machines Download PDFInfo
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- CN110224649A CN110224649A CN201910595422.0A CN201910595422A CN110224649A CN 110224649 A CN110224649 A CN 110224649A CN 201910595422 A CN201910595422 A CN 201910595422A CN 110224649 A CN110224649 A CN 110224649A
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- 238000012706 support-vector machine Methods 0.000 title claims abstract description 40
- 238000000034 method Methods 0.000 title claims abstract description 23
- 239000013598 vector Substances 0.000 claims abstract description 48
- 241001269238 Data Species 0.000 claims 1
- 238000004364 calculation method Methods 0.000 abstract description 7
- 230000010349 pulsation Effects 0.000 abstract description 5
- 230000006870 function Effects 0.000 description 21
- 238000005070 sampling Methods 0.000 description 5
- 238000010606 normalization Methods 0.000 description 3
- 238000002790 cross-validation Methods 0.000 description 2
- 230000001360 synchronised effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000005389 magnetism Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
Classifications
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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
- H02P21/24—Vector control not involving the use of rotor position or rotor speed sensors
- H02P21/28—Stator flux based control
- H02P21/30—Direct torque control [DTC] or field acceleration method [FAM]
-
- 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
- H02P27/00—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage
- H02P27/04—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage using variable-frequency supply voltage, e.g. inverter or converter supply voltage
- H02P27/06—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage using variable-frequency supply voltage, e.g. inverter or converter supply voltage using dc to ac converters or inverters
- H02P27/08—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage using variable-frequency supply voltage, e.g. inverter or converter supply voltage using dc to ac converters or inverters with pulse width modulation
- H02P27/12—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage using variable-frequency supply voltage, e.g. inverter or converter supply voltage using dc to ac converters or inverters with pulse width modulation pulsing by guiding the flux vector, current vector or voltage vector on a circle or a closed curve, e.g. for direct torque 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
- H02P6/00—Arrangements for controlling synchronous motors or other dynamo-electric motors using electronic commutation dependent on the rotor position; Electronic commutators therefor
- H02P6/10—Arrangements for controlling torque ripple, e.g. providing reduced torque ripple
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Control Of Ac Motors In General (AREA)
Abstract
The present invention relates to a kind of methods for being used for DTC PREDICTIVE CONTROL based on support vector machines, first choose existing motor operating parameter as input and output sample；The input of selection and output sample are normalized respectively；Sample after normalized is trained, the prediction model for being used for DTC based on support vector machines of output voltage vector is obtained；It is input in prediction model after the operating parameter of current motor is normalized, the basic voltage vectors applied by prediction model output；According to the basic voltage vectors that prediction model exports, the switch state of inverter is controlled, completes to be used for DTC PREDICTIVE CONTROL based on support vector machines.The present invention can reduce relevant calculation amount, reduce torque pulsation, and switching frequency is constant.
Description
Technical field
The invention belongs to Motor Control Fields, and in particular to a kind of side that DTC PREDICTIVE CONTROL is used for based on support vector machines
Method.
Background technique
Direct Torque Control (DTC) controls electromagnetic torque by controlling amplitude and the power angle of stator magnetic linkage.Permanent magnetism is same
It walks motor Direct Torque Control and controls stator magnetic linkage and torque deviation respectively usually using two hysteresis comparators, since it is avoided
A large amount of calculating under rotating coordinate system, dynamic property can make moderate progress, and response is faster.
In the Direct Torque Control System for Permanent Magnet Synchronous Motor that traditional switch table is realized, voltage vector is a sampling period
It is inside continuously applied, it may appear that actual torque increase and decrease is required beyond expected, so that overshoot be caused to pulse.
In order to solve problems, PREDICTIVE CONTROL is introduced, cost function is introduced, comprehensively considers torque error and stator magnetic linkage
Error, and be controlled, using space vector modulation technique, to reduce torque pulsation.But there are still some problems for PREDICTIVE CONTROL,
The science of its cost function first is still to be studied, is secondly to make torque error and magnetic linkage error in same dimension, it will usually
Weight coefficient is introduced, and the selection of weight coefficient relies on experience mostly, lacks strong theory support.Meanwhile using pre- observing and controlling
Standard needs to calculate the cost of six basic voltage vectors simultaneously, calculation amount it is huge, this and introduce Direct Torque Control
Original intention is disagreed.
Summary of the invention
The purpose of the present invention is to overcome the above shortcomings and to provide a kind of based on support vector machines for DTC PREDICTIVE CONTROL
Method can reduce relevant calculation amount, reduce torque pulsation, and switching frequency is constant.
In order to achieve the above object, the present invention the following steps are included:
The following steps are included:
Step 1: existing motor operating parameter is chosen as input and output sample；
Step 2: the input of selection and output sample are normalized respectively；
Step 3: being trained the sample after normalized, obtain output voltage vector based on support vector machines
Prediction model for DTC；
Step 4: the operating parameter of current motor is normalized；
Step 5: the operating parameter after normalized in step 4 is input in prediction model, prediction is passed through
The basic voltage vectors that model output applies；
Step 6: the basic voltage vectors exported according to prediction model control the switch state of inverter, complete based on branch
Vector machine is held for DTC PREDICTIVE CONTROL.
Further, in step 1, existing motor operating parameter carries out voltage vector selection using cost function
Motor operating parameter, shown in cost function such as following formula (1):
WhereinFor reference amplitude,For torque reference,For actual magnitude, TeFor actual torque.
Further, the number of cost function and the number of basic voltage vectors are equal, choose the smallest cost function institute
Corresponding basic voltage vectors, the voltage vector as applied；Choose the stator magnetic linkage error ψ under full working scoper, output torque
Error Tr, the input of power angle δ and angle of torsion α as training prediction model, choose the voltage vector of application as output.
Further, in step 2, to outputting and inputting the expression formula such as following formula (2) that sample is normalized respectively
It is shown:
Wherein, xminFor minimum value in input sample or output sample, xmaxFor input sample or export in sample most
Big value.
Further, it in step 3, is trained using support vector machines.
Further, using radial basis function as support vector machines kernel function, as shown in formula (3):
K (x, xi)=exp (- γ ‖ x-xi‖2), γ > 0 (3)
Wherein x is input, xiFor supporting vector, γ is nuclear parameter.
Further, the sample that outputs and inputs chosen in step 1 is divided into 3 groups, each group of subset data is distinguished
One-time authentication collection is made, remaining 2 groups of subset data obtains 3 submodels as training set, and each submodel respectively corresponds one
Group penalty parameter c and kernel functional parameter g is used and verifies the average of the classification accuracy of collection in 3 submodels as classifier
Performance indicator.
Further, if the average of classification accuracy is more than or equal to 90%, highest one group of classification accuracy is chosen
Parameter of the parameter c and g as prediction model, if the average of classification accuracy less than 90%, re -training.
Further, in step 4, by the stator magnetic linkage error ψ for obtaining the k momentr, output torque error Tr, power angle δ
After angle of torsion α, it is normalized.
Further, normalized mode is identical with step 2 in step 4.
Compared with prior art, the invention has the following beneficial technical effects:
The present invention uses the operation data of the motor of cost function by obtaining, and is instructed offline using the method for support vector machines
Corresponding prediction model is got, trained model is recycled to carry out the choosing of voltage vector according to the current operating parameter of motor
It selects, to realize the quick control of motor.The property of Direct Torque Control System for Permanent Magnet Synchronous Motor is improved by above method
Can, relevant calculation amount is reduced, reduces torque pulsation, and switching frequency is constant.The present invention is by introducing the good prediction of off-line learning
Model avoids the introducing of cost function compared to PREDICTIVE CONTROL compared to the pulsation that Direct Torque Control reduces torque,
So as to avoid the huge calculation amount of cost function, achieve the purpose that simplified calculation amount, and then optimizes the real-time of motor control
Property.
Detailed description of the invention
Fig. 1 is based on model training figure of the present invention；
Fig. 2 is the principle of the present invention block diagram；
Specific embodiment
The present invention will be further described with reference to the accompanying drawing.
Referring to Fig. 1 and Fig. 2, the present invention is based on support vector machines to be used for DTC forecast Control Algorithm, the specific steps are as follows:
Step 1: based on the parameter for having the motor operation for carrying out voltage vector selection using cost function, it is suitable to determine
Sample frequency, therefrom select for based on support vector machines for DTC PREDICTIVE CONTROL it is suitable input with export sample.
Wherein, the motor of voltage vector selection is carried out using cost function, the expression formula of cost function is such as shown in (1).
WhereinFor reference amplitude,For torque reference,For actual magnitude, TeFor actual torque.
When alternative basic voltage vectors number is 6,6 cost functions will be obtained, the smallest cost is therefrom selected
Basic voltage vectors corresponding to function, the voltage vector as applied.By parameter notes whole in above-mentioned motor operation course
After record is lower, the sampling period is ten times of motor sampling period, chooses the stator magnetic linkage error ψ under full working scoper, output torque error
Tr, the input of power angle δ and angle of torsion α as training prediction model, choose the voltage vector of application as output.
Step 2: the input determined in step 1 and output sample are normalized.
To make to output and input respectively in same magnitude, need that place is normalized respectively to outputting and inputting sample
Reason, i.e., by the x before normalization, normalizing is at y value according to the following formula, shown in expression formula such as formula (2).
Wherein, xminFor minimum value in sample, xmaxFor maximum value in sample；It is normalized to input sample
When, xminFor minimum value in input sample, xmaxFor maximum value in input sample；When output sample is normalized, xmin
For minimum value in output sample, xmaxFor maximum value in output sample.
Step 3: the sample after normalization is trained using support vector machines, obtains the base of output voltage vector
The prediction model of DTC is used in support vector machines.
The sample after normalization is trained using support vector machines.According to the method and thought of support vector machines, choosing
It takes shown in radial basis function such as formula (3), as support vector machines kernel function.
k(x,xi)=exp (- γ ‖ x-xi‖2),γ>0 (3)
Wherein x is input, xiFor supporting vector, γ is nuclear parameter.
The sample that outputs and inputs chosen in step 1 is upset into the new sequence of reformulation at random, to ensure off-line training
Based on support vector machines be used for DTC prediction model accuracy, the method for sampling cross validation, the sample that will be chosen in step 1
Originally it is divided into 3 groups, makees one-time authentication collection respectively for each group, remaining 2 groups of subset data can thus obtains 3 as training set
A submodel, each submodel respectively correspond one group of penalty parameter c and kernel functional parameter g, with verifying collection in 3 submodels
Performance indicator of the average of classification accuracy as classifier.If the average of classification accuracy is more than or equal to 90%, select
Parameter of 3 sub- highest one group of parameters c, g of category of model accuracy rate as prediction model is taken, if classification accuracy is averaged
It counts less than 90%, then re -training.The classification accuracy of its submodel and the average of classification accuracy are calculated such as formula (4) institute
Show.
Wherein ηiFor the classification accuracy of i-th of submodel, ni1To select correct voltage vector in i-th of submodel
Number, ni2For the total number for selecting voltage vector in i-th of submodel, ηaveFor the average of classification accuracy.
Step 4: the operating parameter of current motor is normalized.
By the stator magnetic linkage error ψ for obtaining the k momentr, output torque error Tr, after power angle δ and angle of torsion α, returned
One change processing, same to formula (2).
Step 5: the parameter after normalized in step 4 is input to and is used for DTC's based on support vector machines
In prediction model.Use the trained fundamental voltage applied for the prediction model of DTC to motor based on support vector machines
Vector is predicted.
By treated, data are input in trained supporting vector machine model, by being used for based on support vector machines
The basic voltage vectors that the prediction model output of DTC applies.This process selects basic voltage vectors instead of use cost function.
Step 6: according to the basic voltage vectors exported based on support vector machines for the prediction model of DTC, inversion is controlled
The switch state of device, to realize the operation of motor.
According to direct torque control theory, the fundamental voltage arrow exported based on support vector machines for the prediction model of DTC
Amount determines the switch state of inverter, to control the operating of motor.
Embodiment 1
The parameter that the motor operation of voltage vector selection is carried out using cost function, including stator magnetic linkage error are acquired first
ψr, output torque error Tr, power angle δ, angle of torsion α and selected voltage vector, sample frequency is that motor uses the ten of frequency
Times, the data after sampling are normalized, as shown in formula (2).Use formula (3) as the support vector machines of kernel function into
Row off-line training obtains the highest model of accuracy, the calculation method of accuracy such as formula (4) institute using the method for cross validation
Show, and using the highest model of accuracy as the prediction model for being used for DTC based on support vector machines.
When secondly by motor operation, the stator magnetic linkage error ψ at a certain momentr, output torque error Tr, power angle δ, torque
Angle α by normalized, is input in prediction model as input, obtains this selected fundamental voltage of moment motor
Vector.Inverter switching states are determined according to basic voltage vectors, to control motor.The basic voltage vectors that wherein select with
The state relation of inverter switching device is as shown in Table 1.For example, when the prediction model based on support vector machines for DTC selects voltage
Vector is 0 voltage vector (i.e. u1) when, inverter state SA=1, SB=0, SC=0.
The state relation table of one basic voltage vectors of table and inverter switching device
Claims (10)
1. a kind of method for being used for DTC PREDICTIVE CONTROL based on support vector machines, it is characterised in that: the following steps are included:
Step 1: existing motor operating parameter is chosen as input and output sample；
Step 2: the input of selection and output sample are normalized respectively；
Step 3: being trained the sample after normalized, obtains being used for based on support vector machines for output voltage vector
The prediction model of DTC；
Step 4: the operating parameter of current motor is normalized；
Step 5: the operating parameter after normalized in step 4 is input in prediction model, prediction model is passed through
Export the basic voltage vectors applied；
Step 6: the basic voltage vectors exported according to prediction model control the switch state of inverter, complete based on support to
Amount machine is used for DTC PREDICTIVE CONTROL.
2. a kind of method for being used for DTC PREDICTIVE CONTROL based on support vector machines according to claim 1, it is characterised in that:
In step 1, existing motor operating parameter is the motor operating parameter that voltage vector selection is carried out using cost function, cost
Shown in function such as following formula (1):
WhereinFor reference amplitude,For torque reference,For actual magnitude, TeFor actual torque.
3. a kind of method for being used for DTC PREDICTIVE CONTROL based on support vector machines according to claim 2, it is characterised in that:
The number of cost function and the number of basic voltage vectors are equal, choose the arrow of fundamental voltage corresponding to the smallest cost function
Amount, the voltage vector as applied；Choose the stator magnetic linkage error ψ under full working scoper, output torque error Tr, power angle δ and
Input of the angle of torsion α as training prediction model chooses the voltage vector of application as output.
4. a kind of method for being used for DTC PREDICTIVE CONTROL based on support vector machines according to claim 1, it is characterised in that:
In step 2, to outputting and inputting, the expression formula such as following formula (2) that sample is normalized respectively is shown:
Wherein, xminFor minimum value in input sample or output sample, xmaxFor maximum value in input sample or output sample.
5. a kind of method for being used for DTC PREDICTIVE CONTROL based on support vector machines according to claim 1, it is characterised in that:
In step 3, it is trained using support vector machines.
6. a kind of method for being used for DTC PREDICTIVE CONTROL based on support vector machines according to claim 5, it is characterised in that:
Using radial basis function as support vector machines kernel function, as shown in formula (3):
k(x,xi)=exp (- γ ‖ x-xi‖2),γ>0 (3)
Wherein x is input, xiFor supporting vector, γ is nuclear parameter.
7. a kind of method for being used for DTC PREDICTIVE CONTROL based on support vector machines according to claim 5, it is characterised in that:
The sample that outputs and inputs chosen in step 1 is divided into 3 groups, each group of subset data is made into one-time authentication collection respectively, remaining
2 groups of subset datas as training set, obtain 3 submodels, each submodel respectively corresponds one group of penalty parameter c and core letter
Number parameter g, uses the performance indicator that the average of the classification accuracy of collection is verified in 3 submodels as classifier.
8. a kind of method for being used for DTC PREDICTIVE CONTROL based on support vector machines according to claim 7, it is characterised in that:
If the average of classification accuracy is more than or equal to 90%, highest one group of classification accuracy of parameter c and g are chosen as prediction
The parameter of model, if the average of classification accuracy less than 90%, re -training.
9. a kind of method for being used for DTC PREDICTIVE CONTROL based on support vector machines according to claim 1, it is characterised in that:
In step 4, by the stator magnetic linkage error ψ for obtaining the k momentr, output torque error Tr, after power angle δ and angle of torsion α, carry out
Normalized.
10. a kind of method for being used for DTC PREDICTIVE CONTROL based on support vector machines according to claim 1, it is characterised in that:
Normalized mode is identical with step 2 in step 4.
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Cited By (1)
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| CN111555684A (en) * | 2020-04-03 | 2020-08-18 | 浙江工业大学 | Variable-switching-point multi-step model prediction torque control method for weight-factor-free permanent magnet synchronous motor finite set |
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