CN108964546A - The method for detecting motor rotor position and revolving speed based on adaptive kernel time-frequency distribution - Google Patents
The method for detecting motor rotor position and revolving speed based on adaptive kernel time-frequency distribution 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
- H02P21/0003—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control
- H02P21/0007—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control using sliding mode 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
- H02P21/14—Estimation or adaptation of machine parameters, e.g. flux, current or voltage
- H02P21/18—Estimation of position or speed
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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
- H02P6/00—Arrangements for controlling synchronous motors or other dynamo-electric motors using electronic commutation dependent on the rotor position; Electronic commutators therefor
- H02P6/14—Electronic commutators
- H02P6/16—Circuit arrangements for detecting position
- H02P6/18—Circuit arrangements for detecting position without separate position detecting elements
- H02P6/182—Circuit arrangements for detecting position without separate position detecting elements using back-emf in windings
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- Control Of Motors That Do Not Use Commutators (AREA)
Abstract
The present invention provides a kind of methods for detecting motor rotor position and revolving speed based on adaptive kernel time-frequency distribution, for detecting rotor speed and the position of motor, include the following steps: step 1, constructs sliding mode observer state-space expression, obtain the switching signal of stator current error；Step 2, addition filter removes high-frequency noise, obtains the switching signal of stator current error；Step 3, FO-PLL phaselocked loop is constructed, rotor-position and revolving speed are calculated；And step 4, the stator resistance and inductance parameters of stator that update in sliding mode observer are recognized by improved particle swarm optimization algorithm.The method for detecting motor rotor position and revolving speed based on adaptive kernel time-frequency distribution of the invention can accurately detect brushless DC motor rotor position and revolving speed immediately.
Description
Technical field
Present invention relates particularly to a kind of methods for detecting motor rotor position and revolving speed based on adaptive kernel time-frequency distribution.
Background technique
Motor can be simply controlled by position sensor measurement, but in volume, cost, shell temperature, detection essence
Degree etc. is many-sided all to there are problems that, largely using for sensor increases so that being electrically connected quantity, brings to Anti-interference Design
Certain difficulty.The Hall sensor even most generally used also can not detected normally when the temperature is excessively high, to also limit it
Application range.
Position-sensor-free technology is exactly suggested in the above context.Wherein, electricity can be detected using sliding mode observer
The rotor-position and revolving speed of machine are based on sliding moding structure thought, and according to motor model, the error in usual control circuit is believed
It number changes into after structure changes mode is fed back and to carry out operation, so that reciprocal switching of the system by a certain path or planar high-frequency by a small margin
Movement, is finally eliminated error, variate-value required for exporting.
Traditional sliding mode observer is in actual control system, and entire handoff procedure always exists and time of occurrence and sky
Between on lag, there are a large amount of high-frequency estimation and system operation inertia influence, cause common sliding moding structure to be buffeted
Phenomenon is buffeted then location estimation estimate and subsequent contains a large amount of high frequency, and the presence that can directly influence system comes
Estimate the influence of rotor-position and speed.Furthermore in traditional sliding mode observer, sign function sign (x) is made most of the time
It is used for the control function of observer, and it is a discontinuous piecewise function, it is made to generate chattering phenomenon.
Traditional sliding mode observer does not consider observation error caused by the variation of the parameter of electric machine.As motor is transported for a long time
Row, is influenced, the parameter of electric machine can occur and occur under non-ideality by conditions such as many External Internals such as temperature environment
Many variations gradually cause the result accuracy decline estimated.
Summary of the invention
The present invention is to carry out to solve the above-mentioned problems, and it is an object of the present invention to provide a kind of based on adaptive kernel time-frequency distribution
Immediately the method that can accurately detect brushless DC motor rotor position and revolving speed.
The present invention provides a kind of methods for detecting motor rotor position and revolving speed based on adaptive kernel time-frequency distribution, are used for
Detect rotor speed and the position of motor, which comprises the steps of: step 1, construct motor in the static α β of two-phase
Sliding mode observer state-space expression under coordinate system, obtains the switching signal of the stator current error of motor,
In formula, eα、eβFor the switching signal of stator current error, Rs、LsRespectively stator resistance, stator inductance,
For stator current estimated value, iα、iβFor the current value under the static α β coordinate system of two-phase, uα、uβFor under the static α β coordinate system of two-phase
Voltage value, A are the sliding formwork handoff gain that can be self-regulated, and sign (x) is the sign function for indicating switching function；Step 2, it adds certainly
It adapts to low-pass filter and removes high-frequency noise, obtain smooth back-emf estimation signalIts expression formula are as follows:Wherein,
In formula, ωcFor filter cutoff frequency, ωeFor motor Speed Identification, j is imaginary unit；Step 3, FO- is constructed
PLL phaselocked loop estimates signal according to smooth back-emfTo calculate rotor-positionAnd revolving speed
In formula, ke、kp、ki, r be constant,To estimate the rotor-position obtained after back-emf signal,For fractional order lock
The rotor-position that phase ring estimates, s are Laplace operator；And step 4, by modified particle swarm optiziation (IPSO) come more
New current stator resistance and inductance parameters of stator Rs,Ls。
In the method provided by the invention for detecting motor rotor position and revolving speed based on adaptive kernel time-frequency distribution, may be used also
To have a feature in that wherein, the switching function in step 1 is hyperbolic tangent function, expression formula are as follows:In formula, a is parameter.
In the method provided by the invention for detecting motor rotor position and revolving speed based on adaptive kernel time-frequency distribution, may be used also
To have a feature in that wherein, in step 3,
In formula,For the revolving speed obtained after estimation back-emf signal, ψfFor permanent magnet flux linkage.
In the method provided by the invention for detecting motor rotor position and revolving speed based on adaptive kernel time-frequency distribution, may be used also
To have a feature in that wherein, the expression of the sliding formwork handoff gain A in step 1 are as follows:Formula
In, gcFor modifying factor, l is correction factor, k1For former gain.
In the method provided by the invention for detecting motor rotor position and revolving speed based on adaptive kernel time-frequency distribution, may be used also
To have a feature in that wherein, includes following sub-step: sub-step 4-1 in step 4, population is initialized first, including
Population scale n, iteration sum Z, inertia weight coefficient w, the initial position x for setting particleid0, initial velocity vid0And particle speed
The range bound of degree and position；Sub-step 4-2, to the movement velocity v of each particleidWith position range xidCarry out school
It tests, guarantees speed between safe range [- vmax,vmax] in, spatial position is between controlled range [- xmax,xmax] in, if iteration mistake
Cheng Zhong, particle position xidWith speed vidBeyond boundary value, then the particle position or speed are restricted to maximum speed or boundary bit
It sets.Sub-step 4-3 constructs fitness function, and calculates each particle adaptive value fitness (p)；Sub-step 4-4, to each grain
The particle is currently calculated fitness result and compared with its original individual optimal adaptation value, if current calculated result is excellent by son
In its original individual optimal adaptation value pbest, then original adaptive value is replaced with current calculated result；Sub-step 4-5, to each
Then current calculated result is compared by particle with optimal values original in population, if current calculated result is better than group
Optimum value gbest then replaces original optimum value with current calculated result；Sub-step 4-6 updates grain according to optimizing formula
Sub- speed and position；And sub-step 4-7, judge whether the number of iterations for having reached setting, or met setting
It is expected that adaptive optimal control value, is, terminates iteration and exit circulation, output is related as a result, otherwise returning to sub-step 4-2.
In the method provided by the invention for detecting motor rotor position and revolving speed based on adaptive kernel time-frequency distribution, may be used also
To have a feature in that wherein, the expression formula of the inertia weight w in sub-step 4-1 are as follows:Formula
In, wmax、wminIt is the maximum value and minimum value of w, Q respectivelymax, Q be greatest iteration number, current iteration number respectively.
In the method provided by the invention for detecting motor rotor position and revolving speed based on adaptive kernel time-frequency distribution, may be used also
Fitness function to have a feature in that wherein, in sub-step 4-3 are as follows:
In formula, ω andFor rotor speed actual value and estimated value, θ andFor rotor position angle actual value and estimated value, k
For current iteration moment, a1、a2、a3、a4For scale factor.
In the method provided by the invention for detecting motor rotor position and revolving speed based on adaptive kernel time-frequency distribution, may be used also
To have a feature in that wherein, in step 4-6, optimizing formula includes speed optimizing formula and placement optimization formula, speed
Spend optimizing formula are as follows:
Placement optimization formula are as follows:
In formula,Indicate that the d of kth time iteration particle i velocity vector ties up component, dir is particle distribution density, pid
Component, p are tieed up for the d of particle i optimal adaptation valuegdComponent is tieed up for the d of population optimum value,Indicate i, iteration particle of kth time
Set the d dimension component of vector, r1,r2For arbitrary constant, c1,c2For nonnegative constant.
In the method provided by the invention for detecting motor rotor position and revolving speed based on adaptive kernel time-frequency distribution, may be used also
To have a feature in that wherein, the expression formula of particle distribution density dir are as follows:
In formula, div is Species structure density, dlimitFor Population Control boundary value.
In the method provided by the invention for detecting motor rotor position and revolving speed based on adaptive kernel time-frequency distribution, may be used also
To have a feature in that wherein, the expression formula of Species structure density are as follows:Formula
In, naFor the total population number in space, n is population scale, and d is identification dimension, piz kFor the value on i-th of particle z-dimension of k moment,For the mean value on k moment z-dimension.
The action and effect of invention
The method for detecting motor rotor position and revolving speed based on adaptive kernel time-frequency distribution involved according to the present invention is led to
The stator parameter improved in sliding mode observer and immediate updating observer is crossed, it is of the invention to be detected based on adaptive kernel time-frequency distribution
The method of motor rotor position and revolving speed can accurately detect brushless DC motor rotor position and revolving speed immediately.
Detailed description of the invention
Fig. 1 is brshless DC motor speed torque double closed-loop control system block diagram；
Fig. 2 is provided by the invention based on adaptive kernel time-frequency distribution detection brushless DC motor rotor position and revolving speed
The schematic diagram of method；
Fig. 3 is provided by the invention based on adaptive kernel time-frequency distribution detection brushless DC motor rotor position and revolving speed
Step schematic diagram in method；
Fig. 4 is FO-PLL phaselocked loop isoboles；
Fig. 5 is that the estimated value for detecting rotor revolving speed by the sliding mode observer before improving is obtained with rotor speed actual value
Comparison diagram；
Fig. 6 is that the estimated value for detecting rotor revolving speed by improved sliding mode observer is obtained with rotor speed actual value
Comparison diagram；
Fig. 7 be not by IPSO parameter identify detect the estimated value of rotor revolving speed must be right with rotor speed actual value
Than figure；
Fig. 8 is to identify to compare with rotor speed actual value to detect the estimated value of rotor revolving speed by IPSO parameter
Figure；And
Fig. 9 is that resistance identification process compares figure.
Specific embodiment
It is real below in order to be easy to understand the technical means, the creative features, the aims and the efficiencies achieved by the present invention
The example combination attached drawing method work for detecting motor rotor position and revolving speed based on adaptive kernel time-frequency distribution a kind of to the present invention is applied to have
Body illustrates.
Fig. 1 is brshless DC motor speed torque double closed-loop control system block diagram.
As shown in Figure 1, system is by brshless DC motor, three phase inverter bridge, space vector pulse width modulation (SVPWM), revolving speed
The part such as adjuster, current regulator, improved sliding mode observer, IPSO parameter identification, Field orientable control (FOC) forms,
Wherein Field orientable control part includes Park transformation, Clark transformation, Park inverse transformation.Due to system Speedless sensor, root
It obtains determining under the static α β coordinate system of two-phase after Clark is converted according to the motor stator Current Voltage that three phase inverter bridge exports
Sub- voltage uαuβWith stator current iαiβ, then by being calculated with the adaptive kernel time-frequency distribution for improving population (IPSO) parameter identification
Obtain motor speed and rotor angle location, through speed regulator, current regulator, output signal controls three-phase after FOC and SVPWM
Inverter bridge.Wherein, adaptive kernel time-frequency distribution is presently disclosed technology, remaining is the prior art.
Fig. 2 is provided by the invention based on adaptive kernel time-frequency distribution detection brushless DC motor rotor position and revolving speed
The schematic diagram of method；And Fig. 3 is provided by the invention based on adaptive kernel time-frequency distribution detection brushless DC motor rotor position
Set and the method for revolving speed in step schematic diagram；
As shown in Figure 2 and Figure 3, provided by the invention a kind of based on adaptive kernel time-frequency distribution detection brshless DC motor turn
The method of sub- position and revolving speed includes the following steps: for accurately detecting brushless DC motor rotor position and revolving speed immediately
Step 1, sliding mode observer state-space expression of the motor under the static α β coordinate system of two-phase is constructed, electricity is obtained
The switching signal of the stator current error of machine, sliding mode observer state-space expression are as follows:
In formula, eα、eβFor the switching signal of stator current error, Rs、LsRespectively stator resistance, stator inductance,
For stator current estimated value, iα、iβFor the current value under the static α β coordinate system of two-phase, uα、uβFor under the static α β coordinate system of two-phase
Voltage value, A are the sliding formwork handoff gain that can be self-regulated, and sign (x) is the sign function for indicating switching function.
In above-mentioned expression formula, the expression of sliding formwork handoff gain A are as follows:
A=k1·gc
In formula, gcFor modifying factor, l is correction factor, k1For former gain.
In above-mentioned expression formula, the expression formula of sign (x) switching function are as follows:
In formula, a is adjustable parameter, and when parameter a value becomes larger, the curve of tanh (x) function also can become therewith precipitous；A value
When becoming smaller, function curve can also approach flat.
Step 2, addition adaptive low-pass filters remove high-frequency noise, obtain smooth back-emf estimation signalIts expression formula are as follows:
In above-mentioned expression formula,
In formula, ωcFor filter cutoff frequency, ωeFor motor Speed Identification, j is imaginary unit.
Step 3, such as Fig. 4, FO-PLL phaselocked loop is constructed, signal is estimated according to smooth back-emfTo calculate rotor
PositionAnd revolving speed
Rotor-positionExpression formula are as follows:
Rotor speedExpression formula are as follows:
In above-mentioned expression formula,
In formula, r is order, kpFor proportionality coefficient, kiFor integral coefficient,Turn for what is obtained after estimation back-emf signal
Speed, ψfFor permanent magnet flux linkage,To estimate the rotor-position obtained after back-emf signal,It is estimated for fractional order phaselocked loop
Rotor-position, s are Laplace operator.
Step 4, current stator resistance and inductance parameters of stator R are updated by modified particle swarm optiziation (IPSO)s,Ls,
Including following sub-step:
Sub-step 4-1 first initializes population, including population scale n, iteration sum Z, inertia weight coefficient w, sets
Determine the initial position x of particleid0, initial velocity vid0And the range bound of particle rapidity and position,
Wherein, the expression formula of inertia weight w are as follows:
In formula, wmax、wminIt is the maximum value and minimum value of w, Q respectivelymax, Q be greatest iteration number, current iteration number respectively.
Sub-step 4-2, to the movement velocity v of each particleidWith position range xidIt is verified, guarantees that speed is situated between
In safe range [- vmax,vmax] in, spatial position is between controlled range [- xmax,xmax] in, if in iterative process, particle position
xidWith speed vidBeyond boundary value, then the particle position or speed are restricted to maximum speed or boundary position.
Sub-step 4-3 constructs fitness function, and calculates each particle adaptive value fitness (p), wherein fitness letter
Number are as follows:
In formula, ω andFor rotor speed actual value and estimated value, θ andFor rotor position angle actual value and estimated value, k
For current iteration moment, a1、a2、a3、a4For scale factor.
The particle is currently calculated fitness result and its original individual optimal adaptation value to each particle by sub-step 4-4
It compares, if current calculated result is replaced original better than its original individual optimal adaptation value pbest with current calculated result
Adaptive value.
Then current calculated result is compared each particle by sub-step 4-5 with optimal values original in population,
If current calculated result is better than group's optimum value gbest, original optimum value is replaced with current calculated result.
Sub-step 4-6 updates particle rapidity and position according to optimizing formula, optimizing formula include speed optimizing formula and
Placement optimization formula,
Speed optimizing formula are as follows:
Placement optimization formula are as follows:
In above-mentioned expression formula, the expression formula of particle distribution density dir are as follows:
The expression formula of Species structure density are as follows:
In formula,Indicate that the d of kth time iteration particle i velocity vector ties up component, dir is particle distribution density, pid
Component, p are tieed up for the d of particle i optimal adaptation valuegdComponent is tieed up for the d of population optimum value,Indicate i, iteration particle of kth time
Set the d dimension component of vector, r1,r2For arbitrary constant, c1,c2For nonnegative constant, div is Species structure density, dlimitFor kind
Group control boundary value, naFor the total population number in space, n is population scale, and d is identification dimension, piz kIt is tieed up for k moment particle i in z
Value on degree,For the mean value on k moment z-dimension.
Sub-step 4-7 judges whether the number of iterations for having reached setting, or met setting expectation it is optimal suitable
It should be worth, be, terminate iteration and exit circulation, output is related as a result, otherwise returning to sub-step 4-2.
Fig. 5 is the estimated value and rotor speed reality that rotor revolving speed is detected by the conventional sliding mode observer before improving
It is worth comparison diagram；And Fig. 6 is the estimated value and rotor speed that rotor revolving speed is detected by improved sliding mode observer
Actual value obtains comparison diagram.
As shown in Figure 5, Figure 6, although being both able to carry out rotor angle follows estimation, the estimation of routine observation device there are still
Certain error and buffeting, improved observer position angular estimation are more accurate.
Fig. 7 be not by IPSO parameter identify detect the estimated value of rotor revolving speed must be right with rotor speed actual value
Than figure；And Fig. 8 be by IPSO parameter identify detect the estimated value of rotor revolving speed must be right with rotor speed actual value
Than figure.
As shown in Figure 7, Figure 8, although traditional sliding mode observer itself has certain robustness and Ability of Resisting Disturbance,
Self adjustment is very difficult to for the error of parameter itself.And motor model Parameters variation has a fixing to the performance of observer
It rings.The case where motor is influenced by temperature and resistance parameter is changed in real system is relatively conventional.Optimized using IPSO and is slided
Mould observer SMO system and then the secondary emulation experiment that model stator resistance variations are carried out to system, equally respectively in 0.1s and
It is 0.1 Ω and 0.5 Ω that 0.2s, which changes resistance, and motor estimates revolving speed, and there are the fluctuatings of of short duration time at the time of parameter is mutated
Fluctuation, but speed estimate value again stable can return to given value in a short period of time.Revolving speed is without obvious buffeting or estimation
Error.
Fig. 9 is that resistance identification process compares figure.
As shown in figure 9, improved IPSO algorithm the convergence speed is fast, when identifying motor parameter, modified hydrothermal process iteration is about
It is restrained after 30 times, but PSO algorithm then restrains after offer 50 times or so iteration, common LSM algorithm needs iteration 80 times
It can just be restrained after even more.
The method that motor rotor position and revolving speed are detected based on adaptive kernel time-frequency distribution provided by according to the present invention, because
To detect motor rotor position and revolving speed by improving sliding mode observer, so of the invention examined based on adaptive kernel time-frequency distribution
The method of measured motor rotor-position and revolving speed can accurately detect brushless DC motor rotor position and revolving speed immediately.
The action and effect of embodiment
The method that motor rotor position and revolving speed are detected based on adaptive kernel time-frequency distribution according to involved in the present embodiment,
Because the method containing modified particle swarm optiziation provided through this embodiment is to improve sliding mode observer, simultaneously immediate updating is fixed
Subparameter, so, the method for the invention for detecting motor rotor position and revolving speed based on adaptive kernel time-frequency distribution can be instant
Accurately detect brushless DC motor rotor position and revolving speed.
Above embodiment is preferred case of the invention, the protection scope being not intended to limit the invention.
Claims (10)
1. a kind of method for detecting motor rotor position and revolving speed based on adaptive kernel time-frequency distribution, for detecting the motor
The rotor speed and position, which comprises the steps of:
Step 1, sliding mode observer state-space expression of the motor under the static α β coordinate system of two-phase is constructed, motor is obtained
The switching signal of stator current error,
In formula, eα、eβFor the switching signal of stator current error, Rs、LsRespectively stator resistance, stator inductance,It is fixed
Electron current estimated value, iα、iβFor the current value under the static α β coordinate system of two-phase, uα、uβFor the voltage under the static α β coordinate system of two-phase
Value, A are the sliding formwork handoff gain that can be self-regulated, and sign (x) is the sign function for indicating switching function；
Step 2, addition adaptive low-pass filters remove high-frequency noise, obtain smooth back-emf estimation signalIts
Expression formula are as follows:
Wherein,
In formula, ωcFor filter cutoff frequency, ωeFor motor Speed Identification, j is imaginary unit；
Step 3, FO-PLL phaselocked loop is constructed, signal is estimated according to back-emfTo calculate rotor-positionAnd revolving speed
In formula, ke、kp、ki, r be coefficient,To estimate the rotor-position obtained after back-emf signal,Estimate for fractional order phaselocked loop
The rotor-position of calculating, s are Laplace operator；And
Step 4, presently described stator resistance and inductance parameters of stator R are updated by modified particle swarm optiziation (IPSO)s,Ls。
2. the method according to claim 1 that motor rotor position and revolving speed are detected based on adaptive kernel time-frequency distribution,
It is characterized in that:
Wherein, the switching function in the step 1 is hyperbolic tangent function, expression formula are as follows:
In formula, a is adjustable parameter.
3. the method according to claim 1 that motor rotor position and revolving speed are detected based on adaptive kernel time-frequency distribution,
It is characterized in that
Wherein, in the step 3,
In formula,For the revolving speed obtained after estimation back-emf signal, ψfFor permanent magnet flux linkage.
4. the method according to claim 1 that motor rotor position and revolving speed are detected based on adaptive kernel time-frequency distribution,
It is characterized in that:
Wherein, the expression of the sliding formwork handoff gain A in the step 1 are as follows:
A=k1·gc
In formula, gcFor modifying factor, l is correction factor, k1For former gain.
5. the method according to claim 1 that motor rotor position and revolving speed are detected based on adaptive kernel time-frequency distribution,
It is characterized in that:
Wherein, include following sub-step in the step 4:
Sub-step 4-1 first initializes population, including population scale n, iteration sum Z, inertia weight coefficient w, setting grain
The initial position x of sonid0, initial velocity vid0And the range bound of particle rapidity and position；
Sub-step 4-2, to the movement velocity v of each particleidWith position range xidIt is verified, guarantees speed between safety
Range [- vmax,vmax] in, spatial position is between controlled range [- xmax,xmax] in, if in iterative process, particle position xidAnd speed
Spend vidBeyond boundary value, then the particle position or speed are restricted to maximum speed or boundary position；
Sub-step 4-3 constructs fitness function, and calculates each particle adaptive value fitness (p)；
The particle is currently calculated fitness result and its original individual optimal adaptation value carries out by sub-step 4-4 to each particle
Comparison is fitted if current calculated result is better than its original individual optimal adaptation value pbest with the replacement of current calculated result is original
It should be worth；
Then current calculated result is compared each particle by sub-step 4-5 with optimal values original in population, if
Current calculated result is better than group's optimum value gbest, then original optimum value is replaced with current calculated result；
Sub-step 4-6 updates particle rapidity and position according to optimizing formula；And
Sub-step 4-7 judges whether the number of iterations for having reached setting, or has met the expectation adaptive optimal control of setting
Value is to terminate iteration to exit circulation, and output is related as a result, otherwise returning to sub-step 4-2.
6. the method according to claim 5 that motor rotor position and revolving speed are detected based on adaptive kernel time-frequency distribution,
It is characterized in that:
Wherein, the expression formula of the inertia weight w in the sub-step 4-1 are as follows:
In formula, wmax、wminIt is the maximum value and minimum value of w, Q respectivelymax, Q be greatest iteration number, current iteration number respectively.
7. the method according to claim 5 that motor rotor position and revolving speed are detected based on adaptive kernel time-frequency distribution,
It is characterized in that:
Wherein, the fitness function in the sub-step 4-3 are as follows:
In formula, ω andFor rotor speed actual value and estimated value, θ andFor rotor position angle actual value and estimated value, k is to work as
Preceding iteration moment, a1、a2、a3、a4For scale factor.
8. the method according to claim 5 that motor rotor position and revolving speed are detected based on adaptive kernel time-frequency distribution,
It is characterized in that:
Wherein, in the step 4-6, the optimizing formula includes speed optimizing formula and placement optimization formula,
The speed optimizing formula are as follows:
The placement optimization formula are as follows:
In formula,Indicate that the d of kth time iteration particle i velocity vector ties up component, dir is particle distribution density, pidFor particle
The d of i optimal adaptation value ties up component, pgdComponent is tieed up for the d of population optimum value,Indicate kth time iteration particle i position vector
D tie up component, r1,r2For arbitrary constant, c1,c2For nonnegative constant.
9. the method according to claim 8 that motor rotor position and revolving speed are detected based on adaptive kernel time-frequency distribution,
It is characterized in that:
Wherein, the expression formula of the particle distribution density dir are as follows:
In formula, div is Species structure density, dlimitFor Population Control boundary value.
10. the method according to claim 9 that motor rotor position and revolving speed are detected based on adaptive kernel time-frequency distribution,
It is characterized in that:
Wherein, the expression formula of the Species structure density are as follows:
In formula, naFor the total population number in space, n is population scale, and d is identification dimension, piz kFor on i-th of particle z-dimension of k moment
Value,For the mean value on k moment z-dimension.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109450318A (en) * | 2018-12-11 | 2019-03-08 | 广东工业大学 | A kind of DC wotor speed control method and system |
| CN111693289A (en) * | 2020-06-15 | 2020-09-22 | 西安艾科特声学科技有限公司 | Method and system for identifying rotating speed of aircraft engine |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109450318A (en) * | 2018-12-11 | 2019-03-08 | 广东工业大学 | A kind of DC wotor speed control method and system |
| CN111693289A (en) * | 2020-06-15 | 2020-09-22 | 西安艾科特声学科技有限公司 | Method and system for identifying rotating speed of aircraft engine |
| CN112436771A (en) * | 2020-11-17 | 2021-03-02 | 沈阳工业大学 | PMLSM servo system control method based on fractional order hyperbolic tangent switch function |
| CN112436771B (en) * | 2020-11-17 | 2024-05-14 | 沈阳工业大学 | PMLSM servo system control method based on fractional order hyperbolic tangent switching function |
| CN112928959A (en) * | 2021-02-01 | 2021-06-08 | 安徽工程大学 | Permanent magnet synchronous motor position sensorless control method |
| CN112928959B (en) * | 2021-02-01 | 2022-07-26 | 安徽工程大学 | Permanent magnet synchronous motor position sensorless control method |
| CN113328654A (en) * | 2021-06-07 | 2021-08-31 | 润电能源科学技术有限公司 | Motor starting circuit and parameter determination method, system and device |
| CN117353617A (en) * | 2023-12-06 | 2024-01-05 | 小神童创新科技(广州)有限公司 | Closed loop non-inductive control method for DC brushless motor |
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