CN108718164A - A kind of brshless DC motor vector control system and its construction method - Google Patents

A kind of brshless DC motor vector control system and its construction method Download PDF

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Publication number
CN108718164A
CN108718164A CN201810541010.4A CN201810541010A CN108718164A CN 108718164 A CN108718164 A CN 108718164A CN 201810541010 A CN201810541010 A CN 201810541010A CN 108718164 A CN108718164 A CN 108718164A
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Prior art keywords
brshless
layer
motor
fuzzy
formula
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Chinese (zh)
Inventor
李瑶
王易钱
李德军
魏建宏
李玉鹏
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CHANGZHOU LANLING AUTOMATION EQUIPMENT Co Ltd
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CHANGZHOU LANLING AUTOMATION EQUIPMENT Co Ltd
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Priority to CN201810541010.4A priority Critical patent/CN108718164A/en
Publication of CN108718164A publication Critical patent/CN108718164A/en
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02H—EMERGENCY PROTECTIVE CIRCUIT ARRANGEMENTS
    • H02H7/00—Emergency protective circuit arrangements specially adapted for specific types of electric machines or apparatus or for sectionalised protection of cable or line systems, and effecting automatic switching in the event of an undesired change from normal working conditions
    • H02H7/08—Emergency protective circuit arrangements specially adapted for specific types of electric machines or apparatus or for sectionalised protection of cable or line systems, and effecting automatic switching in the event of an undesired change from normal working conditions for dynamo-electric motors
    • H02H7/085—Emergency protective circuit arrangements specially adapted for specific types of electric machines or apparatus or for sectionalised protection of cable or line systems, and effecting automatic switching in the event of an undesired change from normal working conditions for dynamo-electric motors against excessive load
    • 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
    • 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
    • H02P21/22—Current control, e.g. using a current control loop

Abstract

The present invention relates to a kind of brshless DC motor vector control system and its construction method, the brshless DC motor vector control system includes：Two close cycles adjuster, SVPWM controllers and inverter；The wherein described two close cycles adjuster includes rotational speed regulation outer shroud and current regulation inner ring, and the speed regulator of the rotational speed regulation outer shroud is suitable for using fuzzy neural network controller；One given rotating speed is input to the SVPWM controllers after two close cycles adjuster adjusting；And the SVPWM controllers are suitable for generating control wave, and the control signal by being used as brshless DC motor after the inverter inversion；The brshless DC motor vector control system of the present invention introduces fuzzy neural network on the basis of traditional control method, improves the stability of system, increases the adaptive performance of system.

Description

A kind of brshless DC motor vector control system and its construction method
Technical field
The present invention relates to a kind of combination application being combined both fuzzy neural network and brushless motor vector controlled, tools Body is related to a kind of brshless DC motor vector control system and its construction method.
Background technology
With the development of society, valving is used more and more in life produces, how to realize to valve more Add reliable and effective control that there is prodigious researching value.It is big although having there are many control methods to be suggested now For the valve system of part motor control there is prodigious deficiency, such as anti-interference are poor, adaptive ability is insufficient or does not have Adaptive ability is susceptible to failure etc..With the further development of society, every field controls it requirement and can further carry Height, these disadvantages can be all progressively amplified, and become the factor for hindering development.Due to these insufficient presence, it can make system Reliability reduces, or even generates security risk.Meanwhile system maintenance cost caused by these deficiencies, it will also become one An important factor for restricting development.And fuzzy neural network is introduced, high degree is made up into these deficiencies.
Invention content
The object of the present invention is to provide a kind of brshless DC motor vector control system and its construction methods.
In order to reach foregoing invention purpose, the present invention provides a kind of brshless DC motor vector control systems, including：It is double Closed-loop regulator, SVPWM controllers and inverter；The wherein described two close cycles adjuster includes rotational speed regulation outer shroud and current regulation Inner ring, and the speed regulator of the rotational speed regulation outer shroud is suitable for using fuzzy neural network controller；One given rotating speed is through institute It states after two close cycles adjuster is adjusted and is input to the SVPWM controllers；And the SVPWM controllers are suitable for generating control pulse Signal, and the control signal by being used as brshless DC motor after the inverter inversion.
Further, the fuzzy neural network controller is suitable for instructing fuzzy neural network using historical data sample Practice and learn, to obtain network weight and threshold value initial value；And by using On-line monitor learning algorithm to fuzznet The parameter of network controller is adjusted optimization.
Further, the fuzzy neural network shares four layers of BP networks, is input layer, blurring layer, fuzzy programming respectively Layer and output layer, and recurrent neural member is added in being blurred layer；And neural network is instructed using historical data sample Practice and learn, i.e., in input layer, the input vector x of fuzzy neural network is [e, ec]T, input vector x is converted to [- 1,1] Interval value, then the output node value of the input layer be：
Oi (1)=Ii (1)=xi；
In formula, i=1,2；x1=e, x2=ec；
In being blurred layer, Fuzzy processing is carried out to input variable, each input variable is respectively expressed as Vague language Say variable { PB, PS, ZE, NS, NB }, wherein PB is negative big, and PS is negative small, and ZE zero, NS are just small, and NB is honest；Calculating is asked It takes each component to belong to the membership function of each Fuzzy Linguistic Variable set, membership function, the blurring is indicated with Gaussian function Layer shares ten output nodes, and output node value is：
Oij (2)=uij(xi)=exp [- (Iij (2)-aij 2)2/bij 2]；
In formula, Iij (2)=Oi (1), i=1,2；J=1,2 ..., 5；aijAnd bijIt is the central value and width of Gaussian function respectively Angle value；
Each node in being blurred layer introduces isostructural recurrence link node, so this layer of input node value It should be：
Iij (2)(t)=Oi (1)(t)+Oij (2)(t-1)·rij；
In formula, i=1,2；J=1 ..., 5；rijFor recursive unit connection weight；
Oij (2)(t-1) it is the output valve of this layer of previous last time；
In fuzzy rule layer, graphical symbol " ∏ ", which represents, obscures with operation, and Fuzzy Linguistic Variable is realized with product " * " " now obscuring " operation；Fuzzy rule layer shares 25 input nodes, and input node value is：
Ik (3)=O1k (2)*O2k (2)；
In formula, k1=k2=1,2 ..., 5；K=k1k2=1,2 ..., 25；
The output node value of fuzzy rule layer is：
Ok (3)=Ik (3)；
In output layer, to the output node value march blurring normalized of fuzzy rule layer；It should after processing Layer input value and output valve be respectively：
In formula, ωkIt is the connection weight of fuzzy rule layer and output interlayer.
Further, the parameter of fuzzy neural network controller is adjusted by using On-line monitor learning algorithm excellent Change, i.e.,
Mean square error (MSE) object function defined formula is：
In formula, N is training sample quantity；
E (t) is the instantaneous square error Jing Guo each iteration, and formula is：
In above formula, ud(t) it is the desired output of t moment system；
U (t) is the real output value of t moment system；
The amendment learning algorithm of each parameter takes IGA-BP hybrid algorithms in fuzzy neural network controller, to realize mesh Scalar functions E minimums and parameter aij、bij、rij、ωjk、ωkIt is optimal.
Further, the tach signal of brshless DC motor is detected by number, mould mixing T method speed-measuring methods, and by the rotating speed Feedback quantity of the signal as rotational speed regulation outer shroud；Current sensor is accessed in each circuitry phase of brshless DC motor, to adopt in real time Collect each phase current signal, and using each phase current signal as the feedback quantity of current regulation inner ring；And pass through brshless DC motor Position sensor acquisition rotor magnetic pole relative stator winding position signal.
Another aspect, the present invention also provides a kind of construction methods of brshless DC motor vector control system, including such as Lower step：
Step S1 creates fuzzy neural network controller, determines the structure of fuzzy neural network, utilize historical data sample Fuzzy neural network is trained and is learnt, to obtain network weight and threshold value initial value；
Step S2 optimizes the parameter of fuzzy neural network controller, and is adjusted using On-line monitor learning algorithm Whole optimization, to promote the dynamic property of fuzzy neural network controller；
Step S3 detects the three-phase current, rotating speed and rotor-position of brshless DC motor, using as the brushless dc The feedback quantity of machine vector control system；And
Step S4, the motor temperature protection, overcurrent protection, short circuit for designing the brshless DC motor vector control system are protected Shield, over-and under-voltage protection, power on self test, dead area compensation, rotation-clogging protection and error condition instruction.
Further, the fuzzy neural network shares four layers of BP networks, is input layer, blurring layer, fuzzy programming respectively Layer and output layer, and recurrent neural member is added in being blurred layer；And utilize historical data sample to god in the step S1 The process for being trained and learning through network includes following sub-step：
Step S11, in input layer, the input vector x of fuzzy neural network is [e, ec]T, input vector x is converted to [- 1,1] interval value, then the output node value of the input layer be：
Oi (1)=Ii (1)=xi；
In formula, i=1,2；x1=e, x2=ec；
Step S12 carries out Fuzzy processing, each input variable is respectively indicated in being blurred layer to input variable For Fuzzy Linguistic Variable { PB, PS, ZE, NS, NB }, wherein PB is negative big, and PS is negative small, and ZE zero, NS are just small, and NB is just Greatly；The membership function that each component belongs to each Fuzzy Linguistic Variable set is sought in calculating, indicates membership function with Gaussian function, institute It states blurring layer and shares ten output nodes, output node value is：
Oij (2)=uij(xi)=exp [- (Iij (2)-aij 2)2/bij 2]；
In formula, Iij (2)=Oi (1), i=1,2；J=1,2 ..., 5；aijAnd bijIt is the central value and width of Gaussian function respectively Angle value；
Each node in being blurred layer introduces isostructural recurrence link node, so this layer of input node value It should be：
Iij (2)(t)=Oi (1)(t)+Oij (2)(t-1)·rij；
In formula, i=1,2；J=1 ..., 5；rijFor recursive unit connection weight, Oij (2)(t-1) it is this layer of previous past tense The output valve at quarter；
Step S13, in fuzzy rule layer, graphical symbol " ∏ ", which represents, obscures with operation, is realized with product " * " fuzzy Linguistic variable " now obscures " operation；Fuzzy rule layer shares 25 input nodes, and input node value is：
Ik (3)=O1k (2)*O2k (2)；
In formula, k1=k2=1,2 ..., 5；K=k1k2=1,2 ..., 25；
The output node value of fuzzy rule layer is：
Ok (3)=Ik (3)；
Step S14, in output layer, to the output node value march blurring normalized of fuzzy rule layer； The input value and output valve of this layer are respectively after processing：
In formula, ωkIt is the connection weight of fuzzy rule layer and output interlayer.
Further, the parameter of fuzzy neural network controller is optimized in the step S2, and uses On-line monitor The process that learning algorithm carrys out adjusting and optimizing is as follows：
Mean square error (MSE) object function defined formula is：
In formula, N is training sample quantity；
E (t) is the instantaneous square error Jing Guo each iteration, and formula is：
In above formula, ud(t) it is the desired output of t moment system；
U (t) is the real output value of t moment system；
The amendment learning algorithm of each parameter takes IGA-BP hybrid algorithms in fuzzy neural network controller, to realize mesh Scalar functions E minimums and parameter aij、bij、rij、ωjk、ωkIt is optimal.
Further, the three-phase current of brshless DC motor, the process packet of rotating speed and rotor-position are detected in the step S3 Include following sub-step：
Step S31, electromotive force frequency has following relationship with motor speed in brshless DC motor winding：
In formula：N --- motor speed；
F --- electromotive force frequency；
P --- motor number of pole-pairs；
It is tested the speed using T methods and the period of electromotive force in brshless DC motor winding is measured, then digital by utilization, The tach signal that analog circuit operation is simulated, specially：
Set the square-wave signal that the electromotive force that input signal is brshless DC motor obtains after amplifying shaping, and the square wave The frequency of signal is f0；Tally control is carried out by counter, in the positive half period of square-wave signal, counter is to standard time clock arteries and veins Capable counting is rushed in, stops counting in the negative half-cycle of square-wave signal, and the digital quantity in counter is stored in a latch, then Counter is reset, next cycle is waited for count；
The digital quantity for being locked into latch is directly proportional to square-wave cycle, and physical relationship formula is as follows：
In formula：M is the digital quantity in counter；T is the input square-wave signal period；f0For standard clock frequency；
From the above：
Conversion is carried out to the digital quantity in latch, division circuit is used in combination to seek its inverse, to obtain and electromotive force frequency The directly proportional analog voltage signal of rate, that is, measure the rotating speed of motor；
Step S32 accesses current sensor in each circuitry phase of brshless DC motor, is believed with acquiring each phase current in real time Number；And
Step S33, the installation site sensor in brshless DC motor, to acquire the position of rotor magnetic pole relative stator winding Confidence number.
Further, the method that motor temperature protection is designed in the step S4 is as follows：
Temperature sensor is set in brshless DC motor vector control system, for detection brshless DC motor work When temperature；When the temperature detected exceeds preset temperature range, the brshless DC motor vector control system stops work Make, and sends out corresponding alarm sounds；And
The method that dead area compensation is designed in the step S4 is as follows：
It is compensated using average dead time Time s Compensation, the error in three phase static shafting is determined according to the step S3 Voltage, then the error voltage in two-phase static axial system is become by Clarke brings calculating, and transformation for mula is as follows：
In formula：ΔuαWith Δ uβFor the error voltage in two-phase static axial system；Δuan、ΔubnWith Δ ucnFor three phase static axis Error voltage in system；
Error voltage vector depends on current vector angle, two-phase quiescent current shafting plane is divided into six sectors, each Sector corresponds to an error voltage vector, to obtain the relationship of offset voltage and current vector angle in two-phase static axial system；
Offset voltage depends on the polarity of three-phase current, passes through i in the two-phase rotary axis after Park is converteddAnd iqCome The polarity for judging three-phase current, due to i in two-phase rotary axisdAnd iqFor DC quantity, therefore first to idAnd iqIt is filtered, then By filtered idfAnd iqfTo judge the polarity of three-phase current；
After the polarity for judging three-phase current, by the electric current i in two-phase rotary axisdfAnd iqfBy coordinate inverse transformation Current phasor amplitude and the phase angle under two-phase static axial system can be obtained, formula is as follows：
In formula：iαAnd iβFor the electric current in two-phase rotary axis；
idfAnd iqfFor filtered electric current in two-phase rotary axis；
ω is angular velocity of rotation；ISFor current phasor amplitude；θiFor current vector angle；
According to the obtained current vector angle θ of calculatingi, the fan residing for current phasor is judged in two-phase stationary coordinate system Area, so that it is determined that required offset voltage vector, realizes dead area compensation.
Compared with prior art, the invention has the advantages that the brshless DC motor vector control system of the present invention is adopted Rotational speed regulation outer shroud is controlled instead of traditional PID controller with fuzzy neural network controller, and combines arrow Amount control, large torque output, is provided with stronger adaptive ability and anti-interference ability, greatly reduces brshless DC motor event The probability of happening of barrier reduces the maintenance cost of system.
Description of the drawings
Present invention will be further explained below with reference to the attached drawings and examples.
Fig. 1 is the brshless DC motor vector control system functional block diagram of the present invention；
Fig. 2 is the structure of fuzzy neural network figure of the brshless DC motor vector control system of the present invention；
Fig. 3 is the fuzzy neural network controller optimal control flow of the brshless DC motor vector control system of the present invention Figure；
Fig. 4 is number, the mould mixing T method measuring principle block diagrams of the brshless DC motor vector control system of the present invention；
Fig. 5 is that the number of brshless DC motor vector control system of the present invention, mould mixing T methods test the speed working timing figure；
Fig. 6 be the present invention brshless DC motor vector control system two-phase static axial system in error voltage vector figure.
Specific implementation mode
In conjunction with the accompanying drawings, the present invention is further explained in detail.These attached drawings are simplified schematic diagram, only with Illustration illustrates the basic structure of the present invention, therefore it only shows the composition relevant to the invention.
Embodiment 1
Fig. 1 is the brshless DC motor vector control system functional block diagram of the present invention.
As shown in Figure 1, the present embodiment 1 provides a kind of brshless DC motor vector control system, including：Two close cycles are adjusted Device, SVPWM controllers and inverter；The wherein described two close cycles adjuster includes rotational speed regulation outer shroud and current regulation inner ring, and The speed regulator of the rotational speed regulation outer shroud is suitable for using fuzzy neural network controller；One given rotating speed is through the two close cycles Adjuster is input to the SVPWM controllers after adjusting；And the SVPWM controllers are suitable for generating control wave, and By the control signal for being used as brshless DC motor after the inverter inversion.
Specifically, this brshless DC motor vector control system chooses the design scheme of two close cycles intelligent control, it is exactly right Rotational speed regulation outer shroud and current regulation inner ring are controlled respectively, and the output of rotational speed regulation outer shroud is set as current regulation The given input signal of inner ring, when controlling conducting order and the conducting of three-phase inverter eventually by SVPWM output pwm signals Between, effectively to control and adjust the rotating speed of brshless DC motor.
The fuzzy neural network controller is suitable for that fuzzy neural network is trained and is learned using historical data sample It practises, to obtain network weight and threshold value initial value；And by using On-line monitor learning algorithm to Fuzzy Neural-network Control The parameter of device is adjusted optimization.
Fig. 2 is the structure of fuzzy neural network figure of the brshless DC motor vector control system of the present invention.
As shown in Fig. 2, the fuzzy neural network shares four layers of BP networks, it is input layer, blurring layer, fuzzy rule respectively Layer and output layer are drawn, and recurrent neural member is added in being blurred layer；And using historical data sample to fuzzy neural network It is trained and learns, i.e., in input layer, the input vector x of fuzzy neural network is [e, ec]T, input vector x is converted For [- 1,1] interval value, then the output node value of the input layer is：
Oi (1)=Ii (1)=xi；
In formula, i=1,2；x1=e, x2=ec；
In being blurred layer, Fuzzy processing is carried out to input variable, each input variable is respectively expressed as Vague language Say variable { PB, PS, ZE, NS, NB }, wherein PB is negative big, and PS is negative small, and ZE zero, NS are just small, and NB is honest；Calculating is asked It takes each component to belong to the membership function of each Fuzzy Linguistic Variable set, membership function, the blurring is indicated with Gaussian function Layer shares ten output nodes, and output node value is：
Oij (2)=uij(xi)=exp [- (Iij (2)-aij 2)2/bij 2]；
In formula, Iij (2)=Oi (1), i=1,2；J=1,2 ..., 5；aijAnd bijIt is the central value and width of Gaussian function respectively Angle value；
Each node in being blurred layer introduces isostructural recurrence link node, so this layer of input node value It should be：
Iij (2)(t)=Oi (1)(t)+Oij (2)(t-1)·rij；
In formula, i=1,2；J=1 ..., 5；rijFor recursive unit connection weight；
Oij (2)(t-1) it is the output valve of this layer of previous last time；
In fuzzy rule layer, graphical symbol " ∏ ", which represents, obscures with operation, and Fuzzy Linguistic Variable is realized with product " * " " now obscuring " operation；Fuzzy rule layer shares 25 input nodes, and input node value is：
Ik (3)=O1k (2)*O2k (2)；
In formula, k1=k2=1,2 ..., 5；K=k1k2=1,2 ..., 25；
The output node value of fuzzy rule layer is：
Ok (3)=Ik (3)；
In output layer, the output node value march blurring normalized to fuzzy rule layer is first had to；Place The input value and output valve of this layer are respectively after reason：
In formula, ωkIt is the connection weight of fuzzy rule layer and output interlayer.
Fig. 3 is the fuzzy neural network controller optimal control flow of the brshless DC motor vector control system of the present invention Figure.
As shown in figure 3, for the dynamic property of lifting controller, the present embodiment is using On-line monitor learning algorithm to fuzzy The parameter of nerve network controller carries out real-time adjusting and optimizing, i.e.,
Mean square error (MSE) object function defined formula is：
In formula, N is training sample quantity；
E (t) is the instantaneous square error Jing Guo each iteration, and formula is：
In above formula, ud(t) it is the desired output of t moment system；
U (t) is the real output value of t moment system；
The amendment learning algorithm of each parameter takes IGA-BP hybrid algorithms in the fuzzy neural network controller, with reality Existing object function E minimums and parameter aij、bij、rij、ωjk、ωkIt is optimal.
Fig. 4 is number, the mould mixing T method measuring principle block diagrams of the brshless DC motor vector control system of the present invention.
As shown in figure 4, the present embodiment detects the tach signal of brshless DC motor by number, mould mixing T method speed-measuring methods, And using the tach signal as the feedback quantity of rotational speed regulation outer shroud；Current sense is accessed in each circuitry phase of brshless DC motor Device, to acquire each phase current signal in real time, and using each phase current signal as the feedback quantity of current regulation inner ring；And pass through nothing The position signal of the position sensor acquisition rotor magnetic pole relative stator winding of brushless motor.
Specifically, the detection for three-phase current, current sensor is accessed using in each circuitry phase of brshless DC motor, Three-phase current is sampled, obtains the data information of three-phase current in real time；By the position sensor of brshless DC motor, I Can obtain the position signal of rotor magnetic pole relative stator winding, provide location information for subsequent control.Embodiment 2
On the basis of embodiment 1, the present embodiment 2 provides a kind of structure side of brshless DC motor vector control system Method includes the following steps：
Step S1 creates fuzzy neural network controller, determines the structure of fuzzy neural network, utilize historical data sample Fuzzy neural network is trained and is learnt, to obtain network weight and threshold value initial value；
Step S2 optimizes the parameter of fuzzy neural network controller, and is adjusted using On-line monitor learning algorithm Whole optimization, to promote the dynamic property of fuzzy neural network controller；
Step S3 detects the three-phase current, rotating speed and rotor-position of brshless DC motor, using as the brushless dc The feedback quantity of machine vector control system；And
Step S4, the motor temperature protection, overcurrent protection, short circuit for designing the brshless DC motor vector control system are protected Shield, over-and under-voltage protection, power on self test, dead area compensation, rotation-clogging protection and error condition instruction.
Specifically, the concrete structure about the brshless DC motor vector control system, refers to the interior of embodiment 1 Hold, details are not described herein again.
As shown in Fig. 2, the fuzzy neural network shares four layers of BP networks, it is input layer, blurring layer, fuzzy rule respectively Layer and output layer are drawn, and recurrent neural member is added in being blurred layer；And historical data sample pair is utilized in the step S1 The process that neural network is trained and learns includes following sub-step：
Step S11, in input layer, the input vector x of fuzzy neural network is [e, ec]T, input vector x is converted to [- 1,1] interval value, then the output node value of the input layer be：
Oi (1)=Ii (1)=xi；
In formula, i=1,2；x1=e, x2=ec；
Step S12 carries out Fuzzy processing, each input variable is respectively indicated in being blurred layer to input variable For Fuzzy Linguistic Variable { PB, PS, ZE, NS, NB }, wherein PB is negative big, and PS is negative small, and ZE zero, NS are just small, and NB is just Greatly；The membership function that each component belongs to each Fuzzy Linguistic Variable set is sought in calculating, indicates membership function with Gaussian function, institute It states blurring layer and shares ten output nodes, output node value is：
Oij (2)=uij(xi)=exp [- (Iij (2)-aij 2)2/bij 2]；
In formula, Iij (2)=Oi (1), i=1,2；J=1,2 ..., 5；aijAnd bijIt is the central value and width of Gaussian function respectively Angle value；
Each node in being blurred layer introduces isostructural recurrence link node, so this layer of input node value It should be：
Iij (2)(t)=Oi (1)(t)+Oij (2)(t-1)·rij；
In formula, i=1,2；J=1 ..., 5；rijFor recursive unit connection weight, Oij (2)(t-1) it is this layer of previous past tense The output valve at quarter；
Step S13, in fuzzy rule layer, graphical symbol " ∏ ", which represents, obscures with operation, is realized with product " * " fuzzy Linguistic variable " now obscures " operation；Fuzzy rule layer shares 25 input nodes, and input node value is：
Ik (3)=O1k (2)*O2k (2)；
In formula, k1=k2=1,2 ..., 5；K=k1k2=1,2 ..., 25；
The output node value of fuzzy rule layer is：
Ok (3)=Ik (3)；
Step S14, in output layer, to the output node value march blurring normalized of fuzzy rule layer； The input value and output valve of this layer are respectively after processing：
In formula, ωkIt is the connection weight of fuzzy rule layer and output interlayer.
The parameter of fuzzy neural network controller is optimized in the step S2, and uses On-line monitor learning algorithm The process for carrying out adjusting and optimizing is as follows：
Specifically, as shown in figure 3, in order to lifting controller dynamic property, the present embodiment using On-line monitor study calculate Method carries out real-time adjusting and optimizing to the parameter of fuzzy neural network controller, i.e.,
Mean square error (MSE) object function defined formula is：
In formula, N is training sample quantity；
E (t) is the instantaneous square error Jing Guo each iteration, and formula is：
In above formula, ud(t) it is the desired output of t moment system；
U (t) is the real output value of t moment system；
The amendment learning algorithm of each parameter takes IGA-BP hybrid algorithms in fuzzy neural network controller, to realize mesh Scalar functions E minimums and parameter aij、bij、rij、ωjk、ωkIt is optimal.
The process of the three-phase current of detection brshless DC motor, rotating speed and rotor-position includes following son in the step S3 Step：
Fig. 4 is number, the mould mixing T method measuring principle block diagrams of the brshless DC motor vector control system of the present invention；
Fig. 5 is that the number of brshless DC motor vector control system of the present invention, mould mixing T methods test the speed working timing figure.
Step S31, electromotive force frequency has following relationship with motor speed in brshless DC motor winding：
In formula：N --- motor speed；
F --- electromotive force frequency；
P --- motor number of pole-pairs；
Obviously, as long as measuring the analog voltage signal proportional to electromotive force frequency, you can detect motor speed；This reality It applies example and is tested the speed using the T methods in numerical control system and the period of electromotive force signal in machine winding is measured, then pass through The tach signal simulated with number, analog circuit operation, specially：
As shown in figure 4, the square-wave signal that the electromotive force that input signal V1 is brshless DC motor obtains after amplifying shaping, And the frequency of the square-wave signal is f0；Tally control is carried out by counter, in the positive half period of square-wave signal, counter is to mark Punctual clock is counted, and stops counting in the negative half-cycle of square-wave signal, and the digital quantity deposit one in counter is locked Then storage resets counter, next cycle is waited for count；
In Fig. 5, V2To enter the standard time clock pulse of counter, it follows that being locked into the digital quantity and square wave of latch Period is directly proportional, and physical relationship formula is as follows：
In formula：M is the digital quantity in counter；T is the input square-wave signal period；f0For standard clock frequency；
Therefore have：
Conversion is carried out to the digital quantity in latch, division circuit is used in combination to seek its inverse, to obtain and electromotive force frequency The directly proportional analog voltage signal of rate, that is, measure the rotating speed of motor；Step S32 connects in each circuitry phase of brshless DC motor Enter current sensor, to acquire each phase current signal in real time；And
Step S33, the installation site sensor in brshless DC motor, to acquire the position of rotor magnetic pole relative stator winding Confidence number.
Specifically, the detection for three-phase current, current sensor is accessed using in each circuitry phase of brshless DC motor, Three-phase current is sampled, obtains the data information of three-phase current in real time；By the position sensor of brshless DC motor, I Can obtain the position signal of rotor magnetic pole relative stator winding, provide location information for subsequent control.
In addition, multiple protective has also been devised in structure in the brshless DC motor vector control system of the present embodiment, such as But be not limited to include：Motor temperature protection, short-circuit protection, over-and under-voltage protection, power on self test, dead area compensation, is blocked up at overcurrent protection Rotation protection, error condition instruction, to greatly improve the reliability of system.
The method of wherein motor temperature protection is as follows：
Temperature sensor is set in brshless DC motor vector control system, for detection brshless DC motor work When temperature；When the temperature detected exceeds preset temperature range, the brshless DC motor vector control system stops work Make, and sends out corresponding alarm sounds；And
The method of dead area compensation is as follows：
It is compensated using average dead time Time s Compensation, the error in three phase static shafting is determined according to the step S3 Voltage, then the error voltage in two-phase static axial system is become by Clarke brings calculating, and transformation for mula is as follows：
In formula：ΔuαWith Δ uβFor the error voltage in two-phase static axial system；Δuan、ΔubnWith Δ ucnFor three phase static axis Error voltage in system；
Fig. 6 be the present invention brshless DC motor vector control system two-phase static axial system in error voltage vector figure.
In two-phase static axial system, error voltage vector can be indicated with Fig. 6, and error voltage vector depends on current phasor Two-phase quiescent current shafting plane, can be divided into six sectors by angle, and each sector corresponds to an error voltage vector.
Then the relationship of offset voltage and current vector angle in two-phase static axial system can be obtained, as shown in the table：
In table：uerrIndicate reality output phase voltage and the mean error voltage in a cycle of ideal output phase voltage； udcom1And uqcom1Indicate offset voltage component；
Offset voltage depends on the polarity of three-phase current, if judging current polarity by directly detecting three-phase current, Large error is had in zero crossings, easily causes and accidentally compensates, therefore pass through i in the two-phase rotary axis after Park is convertedd And iqThree-phase current is judged, due to i in two-phase rotary axisdAnd iqFor DC quantity, therefore first to idAnd iqIt is filtered, then by Filtered idfAnd iqfTo judge the polarity of three-phase current；
After the polarity for judging three-phase current, by the electric current i in two-phase rotary axisdfAnd iqfBy coordinate inverse transformation Current phasor amplitude and the phase angle under two-phase static axial system can be obtained, formula is as follows：
In formula：iαAnd iβFor the electric current in two-phase rotary axis；
idfAnd iqfFor filtered electric current in two-phase rotary axis；
ω is angular velocity of rotation；ISFor current phasor amplitude；θiFor current vector angle；
According to the obtained current vector angle θ of calculatingi, the fan residing for current phasor is judged in two-phase stationary coordinate system Area, so that it is determined that required offset voltage vector, realizes dead area compensation.
Further, upon power-up, the brshless DC motor vector control system of the present embodiment can be before being initiated to each The hardware of a part is checked, if the system of discovery is deposited when abnormal, stops activation system, and send out corresponding mistake and carry Show；Motor protecter is installed in systems, is protected for realizing the overcurrent protection of motor, short-circuit protection, over-and under-voltage protection, stall Shield；Various type of errors are set in systems, when errors are detected, send out the type of error prompt of corresponding types.
Fuzzy neural network is combined by the present invention with traditional motor closed-loop control, and uses vector controlled, large torque Output, is provided with stronger adaptive ability and anti-interference ability, greatly reduces the probability of happening of failure, reduce system Maintenance cost；It is brshless DC motor used in system, which uses outer magnetic pole rotor, interior loop wound stator structure, It is small, it is efficient.Using shoe permanent magnetism steel disc, it is bonded rotor internal cavity structure completely, rotor air gap is small so that magnetic resistance is small. Magnetic flux conversion efficiency is high, effectively reduce noise, improves torque output, extends electrical machinery life；And motor is carried out in systems Multiple protective, further reduced fault rate, enhances the reliability of system, use also more safe； Meanwhile motor uses EBS decelerations of electrons and energy feedback, can eliminate motor inertia, reduces dead zone range, improves control accuracy, With latching characteristics；It is run simultaneously provided with fault mode, in any one Hall failure of motor or missing or any one phase In the case that line loss is bad, ensures motor normal operation, improve the practicability of system.The present invention is obscured artificial intelligence field Neural network and electric machines control technology combine, and greatly improve the adaptability and reliability of system, have prior theoretical meaning Justice and actual application value.
It is enlightenment with above-mentioned desirable embodiment according to the present invention, through the above description, relevant staff is complete Various changes and amendments can be carried out without departing from the scope of the technological thought of the present invention' entirely.The technology of this invention Property range is not limited to the contents of the specification, it is necessary to determine its technical scope according to right.

Claims (10)

1. a kind of brshless DC motor vector control system, which is characterized in that including：
Two close cycles adjuster, SVPWM controllers and inverter；Wherein
The two close cycles adjuster includes rotational speed regulation outer shroud and current regulation inner ring, and the rotating speed tune of the rotational speed regulation outer shroud Device is saved to be suitable for using fuzzy neural network controller；
One given rotating speed is input to the SVPWM controllers after two close cycles adjuster adjusting；And
The SVPWM controllers are suitable for generating control wave, and by being used as brushless dc after the inverter inversion The control signal of machine.
2. brshless DC motor vector control system according to claim 1, which is characterized in that
The fuzzy neural network controller is suitable for that fuzzy neural network is trained and is learnt using historical data sample, with Obtain network weight and threshold value initial value；And
Optimization is adjusted to the parameter of fuzzy neural network controller by using On-line monitor learning algorithm.
3. brshless DC motor vector control system according to claim 2, which is characterized in that
The fuzzy neural network shares four layers of BP networks, is input layer respectively, is blurred layer, fuzzy programming layer and output layer, And recurrent neural member is added in being blurred layer；And
Neural network is trained and is learnt using historical data sample, i.e.,
In input layer, the input vector x of fuzzy neural network is [e, ec]T, input vector x is converted into [- 1,1] interval value, then The output node value of the input layer is：
Oi (1)=Ii (1)=xi；
In formula, i=1,2；x1=e, x2=ec；
In being blurred layer, Fuzzy processing is carried out to input variable, each input variable is respectively expressed as fuzzy language change It measures { PB, PS, ZE, NS, NB }, wherein PB is negative big, and PS is negative small, and ZE zero, NS are just small, and NB is honest；Calculating is sought often A component belongs to the membership function of each Fuzzy Linguistic Variable set, indicates that membership function, the blurring layer are total with Gaussian function There are ten output nodes, output node value to be：
Oij (2)=uij(xi)=exp [- (Iij (2)-aij 2)2/bij 2]；
In formula, Iij (2)=Oi (1), i=1,2；J=1,2 ..., 5；aijAnd bijIt is the central value and width value of Gaussian function respectively；
Each node in being blurred layer introduces isostructural recurrence link node, so this layer of input node value is answered For：
Iij (2)(t)=Oi (1)(t)+Oij (2)(t-1)·rij；
In formula, i=1,2；J=1 ..., 5；rijFor recursive unit connection weight；
Oij (2)(t-1) it is the output valve of this layer of previous last time；
In fuzzy rule layer, graphical symbol " ∏ ", which represents, obscures with operation, realizes that Fuzzy Linguistic Variable is " existing with product " * " It is fuzzy " operation；Fuzzy rule layer shares 25 input nodes, and input node value is：
Ik (3)=O1k (2)*O2k (2)；
In formula,；k1=k2=1,2 ..., 5；K=k1k2=1,2 ..., 25；
The output node value of fuzzy rule layer is：
Ok (3)=Ik (3)；
In output layer, to the output node value march blurring normalized of fuzzy rule layer；This layer after processing Input value and output valve are respectively：
In formula, ωkIt is the connection weight of fuzzy rule layer and output interlayer.
4. brshless DC motor vector control system according to claim 3, which is characterized in that
Optimization is adjusted to the parameter of fuzzy neural network controller by using On-line monitor learning algorithm, i.e.,
Mean square error (MSE) object function defined formula is：
In formula, N is training sample quantity；
E (t) is the instantaneous square error Jing Guo each iteration, and formula is：
In above formula, ud(t) it is the desired output of t moment system；
U (t) is the real output value of t moment system；
The amendment learning algorithm of each parameter takes IGA-BP hybrid algorithms in fuzzy neural network controller, to realize target letter Number E minimums and parameter aij、bij、rij、ωjk、ωkIt is optimal.
5. brshless DC motor vector control system according to claim 4, which is characterized in that
The tach signal of brshless DC motor is detected by number, mould mixing T method speed-measuring methods, and using the tach signal as rotating speed Adjust the feedback quantity of outer shroud；
Current sensor is accessed in each circuitry phase of brshless DC motor, to acquire each phase current signal in real time, and each phase is electric Flow feedback quantity of the signal as current regulation inner ring；And
The position signal of rotor magnetic pole relative stator winding is acquired by the position sensor of brshless DC motor.
6. a kind of construction method of brshless DC motor vector control system, which is characterized in that include the following steps：
Step S1 creates fuzzy neural network controller, the structure of fuzzy neural network is determined, using historical data sample to mould Paste neural network is trained and learns, to obtain network weight and threshold value initial value；
Step S2 optimizes the parameter of fuzzy neural network controller, and is adjusted using On-line monitor learning algorithm excellent Change, to promote the dynamic property of fuzzy neural network controller；
Step S3 detects the three-phase current, rotating speed and rotor-position of brshless DC motor, to be sweared as the brshless DC motor The feedback quantity of amount control system；And
Step S4, design the motor temperature protection of the brshless DC motor vector control system, overcurrent protection, short-circuit protection, Over-and under-voltage protection, power on self test, dead area compensation, rotation-clogging protection and error condition instruction.
7. the construction method of brshless DC motor vector control system according to claim 6, which is characterized in that
The fuzzy neural network shares four layers of BP networks, is input layer respectively, is blurred layer, fuzzy programming layer and output layer, And recurrent neural member is added in being blurred layer；And
The process for being trained and learning to neural network using historical data sample in the step S1 includes following sub-step：
Step S11, in input layer, the input vector x of fuzzy neural network is [e, ec]T, input vector x is converted to [- 1, 1] interval value, then the output node value of the input layer be：
Oi (1)=Ii (1)=xi；；
In formula, i=1,2；x1=e, x2=ec；
Step S12 carries out Fuzzy processing, each input variable is respectively expressed as mould in being blurred layer to input variable Paste linguistic variable { PB, PS, ZE, NS, NB }, wherein PB is negative big, and PS is negative small, and ZE zero, NS are just small, and NB is honest；Meter The membership function that each component belongs to each Fuzzy Linguistic Variable set is sought in calculation, and membership function, the mould are indicated with Gaussian function Gelatinization layer shares ten output nodes, and output node value is：
Oij (2)=uij(xi)=exp [- (Iij (2)-aij 2)2/bij 2]；
In formula, Iij (2)=Oi (1), i=1,2；J=1,2 ..., 5；aijAnd bijIt is the central value and width value of Gaussian function respectively；
Each node in being blurred layer introduces isostructural recurrence link node, so this layer of input node value is answered For：
Iij (2)(t)=Oi (1)(t)+Oij (2)(t-1)·rij；
In formula, i=1,2；J=1 ..., 5；rijFor recursive unit connection weight, Oij (2)(t-1) it is this layer of previous last time Output valve；
Step S13, in fuzzy rule layer, graphical symbol " ∏ ", which represents, obscures with operation, and fuzzy language is realized with product " * " Variable " now obscures " operation；Fuzzy rule layer shares 25 input nodes, and input node value is：
Ik (3)=O1k (2)*O2k (2)；
In formula, k1=k2=1,2 ..., 5；K=k1k2=1,2 ..., 25；
The output node value of fuzzy rule layer is：
Ok (3)=Ik (3)；
Step S14, in output layer, to the output node value march blurring normalized of fuzzy rule layer；Processing The input value and output valve of this layer are respectively afterwards：
In formula, ωkIt is the connection weight of fuzzy rule layer and output interlayer.
8. the construction method of brshless DC motor vector control system according to claim 7, which is characterized in that
The parameter of fuzzy neural network controller is optimized in the step S2, and is adjusted using On-line monitor learning algorithm The process of whole optimization is as follows：
Mean square error (MSE) object function defined formula is：
In formula, N is training sample quantity；
E (t) is the instantaneous square error Jing Guo each iteration, and formula is：
In above formula, ud(t) it is the desired output of t moment system；
U (t) is the real output value of t moment system；
The amendment learning algorithm of each parameter takes IGA-BP hybrid algorithms in fuzzy neural network controller, to realize target letter Number E minimums and parameter aij、bij、rij、ωjk、ωkIt is optimal.
9. the construction method of brshless DC motor vector control system according to claim 8, which is characterized in that
The process of the three-phase current of detection brshless DC motor, rotating speed and rotor-position includes following sub-step in the step S3 Suddenly：
Step S31, electromotive force frequency has following relationship with motor speed in brshless DC motor winding：
In formula：N --- motor speed；
F --- electromotive force frequency；
P --- motor number of pole-pairs；
It is tested the speed using T methods and the period of electromotive force in brshless DC motor winding is measured, then by with number, simulation The tach signal that circuit computing is simulated, specially：
Set the square-wave signal that the electromotive force that input signal is brshless DC motor obtains after amplifying shaping, and the square-wave signal Frequency be f0；Carry out tally control by counter, in the positive half period of square-wave signal, counter to standard time clock pulse into Row counts, and stops counting in the negative half-cycle of square-wave signal, and the digital quantity in counter is stored in a latch, then reset Counter waits for next cycle to count；
The digital quantity for being locked into latch is directly proportional to square-wave cycle, and physical relationship formula is as follows：
In formula：M is the digital quantity in counter；T is the input square-wave signal period；f0For standard clock frequency；
From the above：
To in latch digital quantity carry out conversion, be used in combination division circuit ask its inverse, with obtain with electromotive force frequency at The analog voltage signal of direct ratio measures the rotating speed of motor；
Step S32 accesses current sensor in each circuitry phase of brshless DC motor, to acquire each phase current signal in real time； And
Step S33, the installation site sensor in brshless DC motor, the position to acquire rotor magnetic pole relative stator winding are believed Number.
10. the construction method of brshless DC motor vector control system according to claim 9, which is characterized in that
The method that motor temperature protection is designed in the step S4 is as follows：
Temperature sensor is set in brshless DC motor vector control system, when for detection brshless DC motor work Temperature；
When the temperature detected exceeds preset temperature range, the brshless DC motor vector control system is stopped, and Send out corresponding alarm sounds；And
The method that dead area compensation is designed in the step S4 is as follows：
It is compensated using average dead time Time s Compensation, the electricity of the error in three phase static shafting is determined according to the step S3 Pressure, then the error voltage in two-phase static axial system is become by Clarke brings calculating, and transformation for mula is as follows：
In formula：ΔuαWith Δ uβFor the error voltage in two-phase static axial system；Δuan、ΔubnWith Δ ucnFor in three phase static shafting Error voltage；
Error voltage vector depends on current vector angle, and two-phase quiescent current shafting plane is divided into six sectors, each sector A corresponding error voltage vector, to obtain the relationship of offset voltage and current vector angle in two-phase static axial system；
Offset voltage depends on the polarity of three-phase current, passes through i in the two-phase rotary axis after Park is converteddAnd iqTo judge The polarity of three-phase current, due to i in two-phase rotary axisdAnd iqFor DC quantity, therefore first to idAnd iqIt is filtered, then by filtering I after wavedfAnd iqfTo judge the polarity of three-phase current；
After the polarity for judging three-phase current, by the electric current i in two-phase rotary axisdfAnd iqfIt can be obtained by coordinate inverse transformation Current phasor amplitude and phase angle, formula under to two-phase static axial system is as follows：
In formula：iαAnd iβFor the electric current in two-phase rotary axis；
idfAnd iqfFor filtered electric current in two-phase rotary axis；
ω is angular velocity of rotation；ISFor current phasor amplitude；θiFor current vector angle；
According to the obtained current vector angle θ of calculatingi, the sector residing for current phasor is judged in two-phase stationary coordinate system, to It determines required offset voltage vector, realizes dead area compensation.
CN201810541010.4A 2018-05-30 2018-05-30 A kind of brshless DC motor vector control system and its construction method Withdrawn CN108718164A (en)

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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109861607A (en) * 2019-02-25 2019-06-07 常州兰陵自动化设备有限公司 A kind of brshless DC motor vector control system and its construction method
CN109884885A (en) * 2019-02-25 2019-06-14 常州兰陵自动化设备有限公司 A kind of direct current drive apparatus control system and its construction method
CN111585478A (en) * 2020-05-26 2020-08-25 佛山金华信智能科技有限公司 Servo motor driving voltage control method, servo motor driving voltage control device, electronic equipment and storage medium
CN112072961A (en) * 2020-08-27 2020-12-11 吉林大学 Brushless DC motor speed control system based on ANFIS
CN114647183A (en) * 2022-01-21 2022-06-21 苏州浪潮智能科技有限公司 Fan regulation and control method and device for fuzzy control of multiple temperature sensors

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109861607A (en) * 2019-02-25 2019-06-07 常州兰陵自动化设备有限公司 A kind of brshless DC motor vector control system and its construction method
CN109884885A (en) * 2019-02-25 2019-06-14 常州兰陵自动化设备有限公司 A kind of direct current drive apparatus control system and its construction method
CN111585478A (en) * 2020-05-26 2020-08-25 佛山金华信智能科技有限公司 Servo motor driving voltage control method, servo motor driving voltage control device, electronic equipment and storage medium
CN112072961A (en) * 2020-08-27 2020-12-11 吉林大学 Brushless DC motor speed control system based on ANFIS
CN114647183A (en) * 2022-01-21 2022-06-21 苏州浪潮智能科技有限公司 Fan regulation and control method and device for fuzzy control of multiple temperature sensors
CN114647183B (en) * 2022-01-21 2023-12-22 苏州浪潮智能科技有限公司 Fan regulation and control method and device for carrying out fuzzy control on multiple temperature sensors

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