CN110061676B - Bearingless permanent magnet synchronous motor controller based on flux linkage observer - Google Patents

Bearingless permanent magnet synchronous motor controller based on flux linkage observer Download PDF

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CN110061676B
CN110061676B CN201910160397.3A CN201910160397A CN110061676B CN 110061676 B CN110061676 B CN 110061676B CN 201910160397 A CN201910160397 A CN 201910160397A CN 110061676 B CN110061676 B CN 110061676B
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winding
flux linkage
torque
phase
suspension force
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CN110061676A (en
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朱熀秋
颜磊
孙玉坤
杨泽斌
许波
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Jiangsu Daye Environment Co.,Ltd.
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Jiangsu University
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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
    • H02P21/0014—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control using neural networks
    • 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
    • H02P21/141—Flux 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
    • H02P2207/00—Indexing scheme relating to controlling arrangements characterised by the type of motor
    • H02P2207/05—Synchronous machines, e.g. with permanent magnets or DC excitation

Abstract

The invention discloses a bearingless permanent magnet synchronous motor controller based on a flux linkage observer, which is used for controlling direct torque and direct suspension force of a bearingless permanent magnet synchronous motor, and directly controls the torque of the bearingless permanent magnet synchronous motor through a torque winding flux linkage observer, a direct torque controller and a torque winding voltage source inverter; the suspension force of the bearingless permanent magnet synchronous motor is directly controlled through the suspension force winding flux linkage observer, the direct suspension force controller, the suspension force estimation model and the suspension force winding voltage source inverter, the first-order time delay of partial input signals is used as the input of a neural network, the novel flux linkage observer of the torque winding and the suspension force winding is designed through the improved BP neural network, the flux linkage size and the phase of the torque winding, the torque and the flux linkage size and the phase of the suspension winding can be accurately estimated, and the stability of a control system is improved.

Description

Bearingless permanent magnet synchronous motor controller based on flux linkage observer
Technical Field
The invention belongs to the technical field of electric transmission control equipment, and particularly relates to a controller for controlling a bearingless permanent magnet synchronous motor, which is used for controlling the direct torque and the direct suspension force of the bearingless permanent magnet synchronous motor.
Background
The bearingless permanent magnet synchronous motor is a novel special motor with high rotating speed, high precision and no need of lubrication, and has wider and wider application prospect in aerospace aviation, chemical manufacturing, semiconductor industry and other fields requiring special environments. The direct control of the bearingless permanent magnet synchronous motor has the advantages of fast dynamic response, good robustness and the like, comprises direct torque control and direct suspension control, and is widely applied to many fields. Direct control requires flux linkage calculation, and the traditional flux linkage calculation method has low precision and can affect the effect of direct control. Therefore, the improvement of the accuracy of flux linkage calculation has great significance for the direct control of the bearingless permanent magnet synchronous motor.
There are many documents that propose methods for observing flux linkage: the method comprises a direct measurement method based on a detection coil, an electronic voltage-based flux linkage model method, a novel flux linkage integral method, a least square support vector machine, an artificial neural network and the like. The direct measurement method is greatly influenced by noise interference and motor parameter errors; the voltage model method is realized in a pure integration mode, and accumulated errors can be generated to cause the deviation of an integration result; the novel flux linkage integral method is characterized in that an output flux linkage is led back to be fed back on the basis of a first-order low-pass filter, an algorithm between pure integral and low-pass filtering links is established, the control effect is obvious, but the amplitude and the phase of the flux linkage are changed due to the introduction of the low-pass filter, and the flux linkage is more serious at low speed; the least square support vector machine has strong nonlinear expression capability and strong generalization capability, but has the defects of complex structure and large calculation amount, puts forward higher requirements on a digital processing chip in practical application and takes more time; the artificial neural network approximates to a flux linkage observation system of the motor by using excellent fitting capability and has strong learning capability, but the common static neural network lacks necessary feedback and dynamic structure and influences the dynamic performance of the system.
Disclosure of Invention
The invention aims to provide a bearingless permanent magnet synchronous motor controller based on a flux linkage observer, which observes flux linkage information of a torque winding and a suspension force winding based on an improved BP neural network, obtains torque and suspension force required by control through calculation, realizes direct torque control and direct suspension force control of a bearingless permanent magnet synchronous motor, and improves the working performance of direct control of the bearingless permanent magnet synchronous motor.
The technical scheme adopted by the invention is as follows: the direct torque controller outputs a switching signal S1a、S1b、S1cThe direct suspension controller outputting a switching signal S2a、S2b、S2cThe torque winding voltage source inverter outputs the torque winding stator phase current i1a、i1b、i1cThe method is characterized in that: the output end of the direct suspension force controller is respectively connected with a suspension force winding voltage source inverter and a suspension force winding flux linkage observation end, the output end of the suspension force winding voltage source inverter is respectively connected with a bearingless permanent magnet synchronous motor and an input end of the suspension force winding flux linkage observation end, and the suspension force is observedThe input of the winding flux linkage observer is a stator phase current i of a suspension force winding2a、i2b、i2cAnd a switching signal S2a、S2b、S2cAnd observing the magnetic linkage amplitude psi of the suspension force windings2And the output end of the magnetic linkage observation of the suspension force winding is connected with the input end of the direct suspension force controller through the suspension force estimation model; the output end of the direct torque controller is respectively connected with the input ends of a torque winding voltage source inverter and a torque winding magnetic observer, the output end of the torque winding voltage source inverter is respectively connected with the input ends of a bearingless permanent magnet synchronous motor and the torque winding magnetic observer, the output end of the torque winding magnetic observer is respectively connected with the input ends of a suspension force estimation model and the direct torque controller, and the torque winding stator phase current i is input by the torque winding magnetic observer1a、i1b、i1cAnd a switching signal S1a、S1b、S1cAnd the flux linkage amplitude psi of the torque winding is observeds1Sum phase theta, composite air gap flux linkage amplitude psim1And phase μ and torque Te(ii) a The suspension force estimation model is based on the amplitude psi of the synthetic air gap flux linkagem1The sum phase mu and the amplitude phi of the magnetic linkage of the levitation force windings2Calculating the suspension force F by the sum phase lambdaαAnd Fβ。
Further, the torque winding flux linkage observer consists of a first Clark transformation module, a first voltage calculation module, a first BP neural network module, a torque winding flux linkage amplitude phase observation module and a torque observation model, and the torque winding stator group current i1a、i1b、i1cInputting the current component to a first Clark conversion module, and obtaining a current component i under a two-phase static coordinate system after Clark conversion1α、i1βD.c. supply voltage U of torque winding1DCAnd a switching signal S1a、S1b、S1cInputting the voltage to a voltage calculation module, and calculating to obtain a voltage component u under a two-phase static coordinate system1α、u1β(ii) a Current component i1αAnd a voltage component u1αRespectively carrying out first-order time delay to obtain first-order time delay current
Figure BDA0001984419970000021
And first order delay voltage
Figure BDA0001984419970000022
First order time delay current
Figure BDA0001984419970000023
And first order delay voltage
Figure BDA0001984419970000024
And a current component i1βAnd a voltage component u1βThe output of the first BP neural network module is the flux linkage component psi of the torque winding on the two-phase static coordinate systems1α、ψs1βThe output end of the BP neural network module is respectively connected with the torque winding flux linkage amplitude phase observation module and the torque observation model, and the torque winding flux linkage amplitude phase observation module outputs the torque winding flux linkage amplitude psis1Phase theta, amplitude psi of air gap flux linkage synthesized by torque windingsm1And phase mu, torque T output by the torque observation modele
Furthermore, the levitation force winding flux linkage observer consists of a second Clark conversion module, a second voltage calculation module, a second BP neural network module and a levitation force winding flux linkage amplitude phase observation module, wherein the output ends of the second Clark conversion module and the second voltage calculation module are connected with the input end of the second BP neural network module, the output end of the second BP neural network module is connected with the input end of the levitation force winding flux linkage amplitude phase observation module, and the input end of the second Clark conversion module is the levitation force winding stator phase current i2a、i2b、i2cStator phase current i of suspension force winding2a、i2b、i2cObtaining a current component i under a two-phase static coordinate system after Clark conversion2α、i2βThe input of the second voltage calculation module is a suspension force winding direct-current power supply voltage U2DCAnd a switching signal S2a、S2b、S2cD.c. power supply voltage U of levitation winding2DCAnd switch signalNumber S2a、S2b、S2cThe voltage component u under the two-phase static coordinate system is obtained through calculation2α、u2βA current component i2αAnd a voltage component u2αFirst-order delay current obtained by first-order delay
Figure BDA0001984419970000031
And first order delay voltage
Figure BDA0001984419970000032
The output of the second BP neural network module is the flux linkage component psi of the suspension force winding flux linkage on the two-phase static coordinate system as the input of the second BP neural network modules2α、ψs2βFlux linkage component psi of the levitation force windings2α、ψs2βThe amplitude psi of the suspension force winding flux linkage is output by the suspension force winding flux linkage amplitude phase calculation modules2And a phase λ.
The invention has the beneficial effects that:
1. the torque of the bearingless permanent magnet synchronous motor is directly controlled through a torque winding flux linkage observer, a direct torque controller and a torque winding voltage source inverter; the suspension force of the bearingless permanent magnet synchronous motor is directly controlled through a suspension force winding flux linkage observer, a direct suspension force controller, a suspension force estimation model and a suspension force winding voltage source inverter. The neural network system adopted by the invention has simple working principle, can effectively realize the approximate fitting of the dynamic and reciprocating motor flux linkage system, and can be obtained by programming of a digital control chip, thereby being convenient to control.
2. In general, a multilayer forward network is mostly adopted to approximate a nonlinear system by using a neural network, but the network cannot reflect the dynamic performance of the system. The invention provides an improved method for enabling a neural network to better approach a dynamic system and have dynamic characteristics, wherein a first-order delay of partial input signals is used as the input of the neural network, a novel flux linkage observer of a torque winding and a suspension force winding is designed by adopting an improved BP neural network, the flux linkage size and the phase of the torque winding, the torque, the flux linkage size and the phase of the suspension winding can be accurately estimated, and the stability of a control system is improved.
3. The flux linkage observation module replaces the traditional flux linkage calculation method, improves the robustness of flux linkage estimation, is used for direct control of the bearingless permanent magnet synchronous motor, and improves the stability of a control system.
Drawings
FIG. 1 is a block diagram of a bearingless permanent magnet synchronous motor controller based on a flux linkage observer according to the present invention;
fig. 2 is a block diagram of the structure of the torque winding flux linkage observer 1 in fig. 1;
FIG. 3 is a block diagram of the levitation force winding flux linkage observer 2 in FIG. 1;
FIG. 4 is a block diagram of the BP neural network module 12 of FIG. 2;
fig. 5 is a block diagram of the BP neural network module 22 in fig. 3.
In the figure: 1. a torque winding magnetic observer; 2. observing a magnetic linkage of the suspension force winding; 3. a direct torque controller; 4. a direct levitation force controller; 5. a torque winding voltage source inverter; 6. a suspension force estimation model; 7. a levitation force winding voltage source inverter; 8. a photoelectric coding disc; 10. a first Clark transformation module; 11. calculating a first voltage; 12. a first BP neural network module; 13. a torque winding flux linkage amplitude phase observation module; 14. a torque observation model; 20. a second Clark transformation module; 21. a second voltage calculation module; 22. a second BP neural network module; 23. a suspension force winding flux linkage amplitude phase observation module; 31. a first PI controller; 32. a second PI controller; 33. a reference flux linkage generation module; 34. a first space vector pulse width modulation module; 41. a first PID controller; 42. a second PID controller; 43. a force/flux linkage conversion module; 44. a second space vector pulse width modulation module; z-1Representing a first order delay.
Detailed Description
Referring to fig. 1, the bearingless permanent magnet synchronous motor controller based on the flux linkage observer of the invention is composed of a torque winding flux linkage observer 1, a levitation force winding flux linkage observation 2, a direct torque controller 3, a direct levitation force controller 4, a torque winding voltage source inverter 5, a levitation force estimation model 6 and a levitation force winding voltage source inverter 7.
The direct suspension controller 4 outputs a switching signal S2a、S2b、S2cThe output end of the direct suspension force controller 4 is respectively connected with the suspension force winding voltage source inverter 7 and the suspension force winding flux linkage observation 2. The suspension force winding voltage source inverter 7 outputs the stator phase current i of the suspension force winding2a、i2b、i2cThe output end of the suspension force winding voltage source inverter 7 is respectively connected with the input ends of the bearingless permanent magnet synchronous motor and the suspension force winding flux linkage observation 2. The stator phase current i of the suspension force winding is input by the suspension force winding flux linkage observer 22a、i2b、i2cAnd a switching signal S2a、S2b、S2cAnd observing the magnetic linkage amplitude psi of the suspension force windings2And a phase λ. The output end of the levitation force winding flux linkage observation device 2 is connected with the input end of the direct levitation force controller 4 through the levitation force estimation model 6.
The direct torque controller 3 outputs a switching signal S1a、S1b、S1cThe output end of the direct torque controller 3 is respectively connected with the input ends of a torque winding voltage source inverter 5 and a torque winding magnetic observer 1, and the torque winding voltage source inverter 5 outputs a torque winding stator phase current i1a、i1b、i1cThe output end of the torque winding voltage source inverter 5 is respectively connected with the input ends of the bearingless permanent magnet synchronous motor and the torque winding magnetic observer 1, and the output end of the torque winding magnetic observer 1 is respectively connected with the input ends of the suspension force estimation model 6 and the direct torque controller 3. The torque winding flux linkage observer 1 inputs the torque winding stator phase current i1a、i1b、i1cAnd a switching signal S1a、S1b、S1cAnd the flux linkage amplitude psi of the torque winding is observeds1Sum phase theta, composite air gap flux linkage amplitude psim1And phase μ and torque Te。
Suspension force estimation model 6 based onSynthetic air gap flux linkage amplitude psim1The sum phase mu and the amplitude phi of the magnetic linkage of the levitation force windings2Calculating the suspension force F on line by the sum phase lambdaαAnd Fβ。
The switching signal generated by the direct torque controller 3 drives the inverter to directly control the torque winding flux linkage and the torque. The direct torque controller 3 is formed by sequentially connecting a first PI controller 31, a second PI controller 32, a reference flux linkage generation module 33 and a first space vector pulse width modulation module 34 in series. The first space vector pulse width modulation module 34 outputs a switching signal S1a、S1b、S1cThe output of the first space vector pulse width modulation module 34 is connected to the input of the torque winding voltage source inverter 5 and the torque winding magnetic observer 1, respectively. The torque winding voltage source inverter 5 outputs the torque winding stator phase current i of the bearingless permanent magnet synchronous motor1a、i1b、i1c。
The real-time rotating speed omega of the motor is detected by the photoelectric encoding disk 8, and the real-time rotating speed omega and the rotating speed instruction value omega are detected*Comparing, modulating the compared difference value by the first PI controller 31 to generate a torque command value Te *Torque command value Te *And the torque T observed by the torque winding flux linkage observer 1eComparing, modulating the compared difference value by a second PI controller 32 to generate a torque winding flux linkage phase angle delta, and increasing the torque winding flux linkage phase angle delta and a torque winding flux linkage amplitude instruction value
Figure BDA0001984419970000051
And the torque winding flux amplitude psi observed by the torque winding flux observer 1s1And the phase θ as an input of the reference flux linkage generation module 33, the reference flux linkage generation module 33 generating the voltage command value uαAnd uβCommand value u of voltageαAnd uβThe input of the first space vector pulse width modulation module 34 is modulated by the first space vector pulse width modulation module 34 to generate a switching signal, i.e., a three-phase duty ratio, of the voltage source inverter 5, so as to drive the voltage source inverter 5 to realize direct torque control of the bearingless permanent magnet synchronous motor.
The direct suspension controller 4 is composed of a first PID controller 41, a second PID controller 42, a force/flux linkage conversion module 43 and a second space vector pulse width modulation module 44, wherein the output ends of the first PID controller 41 and the second PID controller 42 are connected with the input end of the force/flux linkage conversion module 43, the force/flux linkage conversion module 43 is connected with the second space vector pulse width modulation module 44 in series, the output end of the second space vector pulse width modulation module 44 is respectively connected with the suspension force winding voltage source inverter 7 and the suspension force winding flux linkage observation 2, and the second space vector pulse width modulation module 44 outputs a switching signal S2a、S2b、S2c. The suspension force winding voltage source inverter 7 outputs the stator phase current i of the suspension force winding of the bearingless permanent magnet synchronous motor2a、i2b、i2c。
Obtaining rotor radial displacement values x and y of the bearingless permanent magnet synchronous motor by a radial displacement sensor, and respectively comparing the rotor radial displacement values x and y with a rotor position command value x*And y*Comparing, modulating the obtained difference values by the corresponding first PID controller 41 and the second PID controller 42 to generate a suspension force instruction value
Figure BDA0001984419970000052
And
Figure BDA0001984419970000053
will suspend the force instruction value
Figure BDA0001984419970000054
And
Figure BDA0001984419970000055
respectively outputs the suspension force F with the suspension force estimation model 6αAnd FβIs compared, the compared difference value is converted into a suspension force winding flux linkage increment △ psi by the force/flux linkage conversion module 43s2α、△ψs2βFlux linkage increment of levitating force winding △ psis2α、△ψs2βThen modulated by the space vector pulse width modulation module 44 to obtain a switching signal S2a、S2b、S2cDriving voltage source inversionThe device 7 realizes the direct suspension force control of the bearingless permanent magnet synchronous motor.
The mathematical model of the bearingless permanent magnet synchronous motor flux linkage under the two-phase static coordinate system is as follows:
Figure BDA0001984419970000056
Figure BDA0001984419970000057
in the formula, #s1α、ψs1βIs the flux linkage component of the torque winding on the two-phase stationary coordinate system; psis2α、ψs2βIs the flux linkage component of the suspension force winding on the two-phase static coordinate system; u. of1α、u1βIs the voltage component of the torque winding on the two-phase stationary frame; i.e. i1α，i1βIs the current component of the torque winding in the two-phase stationary coordinate system; u. of2α，u2βVoltage components of the suspension force winding on the two-phase static coordinate system; i.e. i2α，i2βIs the current component of the suspension force winding on the two-phase static coordinate system; rs is the motor stator resistance.
Referring to fig. 2, the torque winding flux linkage observer 1 is composed of a first Clark transformation module 10, a first voltage calculation module 11, a first BP neural network module 12, a torque winding flux linkage amplitude phase observation module 13, and a torque observation model 14. The input of the torque winding flux linkage observer 1 is a torque winding stator group current i1a、i1b、i1cDC supply voltage U of torque winding1DCAnd a switching signal S1a、S1b、S1cAnd the output is torque winding flux linkage amplitude psis1Phase theta, amplitude psi of air gap flux linkage synthesized by torque windingsm1Phase μ, torque Te。
Torque winding stator pack current i1a、i1b、i1cInput to a first Clark transformation module 10, and subjected to Clark transformation to obtain a current component i under a two-phase static coordinate system1α、i1β. Torque winding DC power supplyVoltage U1DCAnd a switching signal S1a、S1b、S1cInput to the first voltage calculation module 11, and the voltage component u under the two-phase static coordinate system is obtained through calculation1α、u1β. The calculation formulas are respectively as follows:
Figure BDA0001984419970000061
Figure BDA0001984419970000062
the output ends of the first Clark conversion module 10 and the first voltage calculation module 11 are both connected with the input end of the first BP neural network module 12. The obtained current component i1αAnd a voltage component u1αRespectively carrying out a first-order delay Z-1Respectively obtain a first-order delay current
Figure BDA0001984419970000063
And first order delay voltage
Figure BDA0001984419970000064
First order time delay current
Figure BDA0001984419970000065
And first order delay voltage
Figure BDA0001984419970000066
And a current component i1βAnd a voltage component u1βThe output of the first BP neural network module 12 is the flux linkage component psi of the torque winding on the two-phase static coordinate systems1α、ψs1β. The output end of the first BP neural network module 12 is respectively connected with a torque winding flux amplitude phase observation module 13 and a torque observation model 14, and the torque winding flux amplitude phase observation module 13 outputs a torque winding flux amplitude psis1Phase theta, amplitude psi of air gap flux linkage synthesized by torque windingsm1And phase μ, the torque observation model 14 outputs the torque Te。
The amplitude phase observation module 13 of the torque winding flux linkage obtains the amplitude psi of the torque winding flux linkage by the following formulas1Sum phase θ, amplitude ψ of the resultant flux linkagem1And phase μ:
Figure BDA0001984419970000071
in the formula i1α，i1βIs the current component of the torque winding in the two-phase stationary coordinate system; psis1θ is the magnitude and phase of the torque winding flux linkage; l is1lIs stator torque winding leakage inductance; psis1α、ψs1βIs the flux linkage component of the torque winding in the two-phase stationary coordinate system; psim1α、ψm1βThe flux linkage component of the torque winding synthetic flux linkage under the two-phase static coordinate system; psim1And μ is the magnitude and phase of the torque winding composite flux linkage.
The torque observation model 14 obtains the torque T by the following equatione：
Te＝1.5pn(ψs1αi1β-ψs1βi1α)，
In the formula, pnIs the pole pair number of the torque winding; psis1α、ψs1βIs the flux linkage component of the torque winding in the two-phase stationary coordinate system; i.e. i1α，i1βIs the current component of the torque winding in the two-phase stationary frame.
Referring to fig. 1 and 3, the levitation force winding flux linkage observer 2 is composed of a second Clark transformation module 20, a second voltage calculation module 21, a second BP neural network module 22, and a levitation force winding flux linkage amplitude phase observation module 23. The output ends of the second Clark transformation module 20 and the second voltage calculation module 21 are both connected to the input end of the second BP neural network module 22, and the output end of the second BP neural network module 22 is connected to the input end of the levitation force winding flux linkage amplitude phase observation module 23.
The output end of the suspension force winding voltage source inverter 7 is connected with a second Clark conversion module 20, and the input of the second Clark conversion module 20 is the stator phase electricity of the suspension force windingStream i2a、i2b、i2cStator phase current i of suspension force winding2a、i2b、i2cObtaining a current component i under a two-phase static coordinate system after Clark conversion2α、i2β. The input of the second voltage calculation module 21 is the levitation force winding dc supply voltage U2DCAnd a switching signal S2a、S2b、S2cD.c. power supply voltage U of levitation winding2DCAnd a switching signal S2a、S2b、S2cThe voltage component u under the two-phase static coordinate system is obtained through calculation2α、u2β. The calculation formulas are respectively as follows:
Figure BDA0001984419970000072
Figure BDA0001984419970000073
the obtained current component i2αAnd a voltage component u2αBy a first delay Z-1The obtained first-order delay current
Figure BDA0001984419970000074
And first order delay voltage
Figure BDA0001984419970000081
The magnetic flux linkage component psi of the suspension force winding magnetic flux linkage on the two-phase static coordinate system is output by the second BP neural network module 22 as the input of the second BP neural network module 22s2α、ψs2β. Flux linkage component psi of the levitation force windings2α、ψs2βThe amplitude psi of the suspension force winding flux linkage is output by the suspension force winding flux linkage amplitude phase calculation module 23 as the input of the suspension force winding flux linkage amplitude phase calculation module 23s2And a phase λ. The suspension force winding flux linkage amplitude phase calculation module 23 obtains its output according to the following formula:
Figure BDA0001984419970000082
in the formula, #s2λ is the amplitude and phase of the levitation force winding flux linkage, #s2α、ψs2βIs the flux linkage component of the levitation force winding in the two-phase static coordinate system.
The training sample of the first BP neural network module 12 in the invention is obtained by a direct torque and direct suspension force simulation system of the bearingless permanent magnet synchronous motor based on MATLAB. In order to reduce the influence of the stator resistance, let the stator resistance RsThe actual working condition is simulated along with the change of time. Collecting torque winding voltage u of simulation model when stator resistance changes along with time1α，u1βAnd current i1α，i1βAnd a current i1αSum voltage signal u1αIs delayed by a first order to obtain
Figure BDA0001984419970000083
And
Figure BDA0001984419970000084
and the flux linkage sample is obtained by the traditional flux linkage observation model, so that the training sample of the neural network is obtained
Figure BDA0001984419970000085
Then carrying out normalization processing on the training sample, and processing the processed i1α，i1β，u1α，u1β，
Figure BDA0001984419970000086
As input to the BP neural network, #s1α、ψs1βAnd performing offline training on neural network parameters as target output of the neural network, wherein after hundreds of times of training, the fitting error of the BP neural network to data is less than 0.001, and finally, the BP neural network module 12 is generated firstly. The neural network adopted by the invention is a three-layer BP neural network with 6 inputs and 2 outputs, and the structure is shown in figure 4. Similarly, the neural network module in the magnetic linkage observation of the levitation force winding can be trained to generate the second BP neural network module 22, which has the structure shown in fig. 5。

Claims (5)

1. A bearingless permanent magnet synchronous motor controller based on a flux linkage observer comprises a direct torque controller (3), a direct suspension controller (4), a torque winding voltage source inverter (5) and a suspension winding voltage source inverter (7), wherein a switching signal S is output by the direct torque controller (3)1a、S1b、S1cThe direct suspension controller (4) outputs a switching signal S2a、S2b、S2cThe torque winding voltage source inverter (5) outputs a torque winding stator phase current i1a、i1b、i1cThe output end of the direct suspension force controller (4) is respectively connected with a suspension force winding voltage source inverter (7) and a suspension force winding flux linkage observation device (2), the output end of the suspension force winding voltage source inverter (7) is respectively connected with the input ends of the bearingless permanent magnet synchronous motor and the suspension force winding flux linkage observation device (2), and the suspension force winding flux linkage observation device (2) inputs the suspension force winding stator phase current i2a、i2b、i2cAnd a switching signal S2a、S2b、S2cAnd observing the magnetic linkage amplitude psi of the suspension force windings2And the phase lambda, the output end of the suspension force winding flux linkage observation (2) is connected with the input end of the direct suspension force controller (4) through the suspension force estimation model (6); the output end of the direct torque controller (3) is respectively connected with the input ends of a torque winding voltage source inverter (5) and a torque winding magnetic observer (1), the output end of the torque winding voltage source inverter (5) is respectively connected with the input ends of a bearingless permanent magnet synchronous motor and the torque winding magnetic observer (1), the output end of the torque winding magnetic observer (1) is respectively connected with the input ends of a suspension force estimation model (6) and the direct torque controller (3), and the torque winding flux linkage observer (1) inputs a torque winding stator phase current i1a、i1b、i1cAnd a switching signal S1a、S1b、S1cAnd the flux linkage amplitude psi of the torque winding is observeds1Sum phase theta, composite air gap flux linkage amplitude psim1And phase μ and torque Te(ii) a The suspension force estimation model (6) is used for estimating the amplitude psi of the magnetic linkage of the synthetic air gapm1And phase mu and levitationForce winding flux linkage amplitude psis2Calculating the suspension force F by the sum phase lambdaαAnd FβThe method is characterized in that: the torque winding flux linkage observer (1) comprises a first Clark transformation module (10), a first voltage calculation module (11), a first BP neural network module (12), a torque winding flux linkage amplitude phase observation module (13) and a torque observation model (14), and the current i of a torque winding stator group1a、i1b、i1cInput into a first Clark transformation module (10), and obtain a current component i under a two-phase static coordinate system after Clark transformation1α、i1βD.c. supply voltage U of torque winding1DCAnd a switching signal S1a、S1b、S1cInput into a voltage calculation module (11) to obtain a voltage component u under a two-phase static coordinate system through calculation1α、u1β(ii) a Current component i1αAnd a voltage component u1αRespectively carrying out first-order time delay to obtain first-order time delay current
Figure FDA0002525636820000011
And first order delay voltage
Figure FDA0002525636820000012
First order time delay current
Figure FDA0002525636820000013
And first order delay voltage
Figure FDA0002525636820000014
And a current component i1βAnd a voltage component u1βThe magnetic flux linkage components psi of the torque winding in the two-phase static coordinate system are taken as the input of the first BP neural network module (12) together, and the output of the first BP neural network module (12) is the magnetic flux linkage component psi of the torque winding in the two-phase static coordinate systems1α、ψs1βThe output end of the BP neural network module (12) is respectively connected with a torque winding flux linkage amplitude phase observation module (13) and a torque observation model (14), and the torque winding flux linkage amplitude phase observation module (13) outputs a torque winding flux linkage amplitude psis1Phase theta, amplitude psi of air gap flux linkage synthesized by torque windingsm1And phaseMu, the torque observed model (14) outputs the torque Te。
2. The bearingless permanent magnet synchronous motor controller based on the flux linkage observer as claimed in claim 1, wherein:
Figure FDA0002525636820000015
the amplitude phase observation module (13) of the torque winding flux linkage obtains the amplitude psi of the torque winding flux linkage through the following formulas1Sum phase θ, amplitude ψ of the resultant flux linkagem1And phase μ:
Figure FDA0002525636820000021
L1lis stator torque winding leakage inductance; psis1α、ψs1βIs the flux linkage component of the torque winding in the two-phase stationary coordinate system; psim1α、ψm1βThe flux linkage component of the torque winding synthetic flux linkage under the two-phase static coordinate system;
passing formula T of torque observation model (14)e＝1.5pn(ψs1αi1β-ψs1βi1α) To obtain a torque Te，pnIs the pole pair number of the torque winding.
3. The bearingless permanent magnet synchronous motor controller based on the flux linkage observer as claimed in claim 1, wherein: the suspension force winding flux linkage observer (2) consists of a second Clark conversion module (20), a second voltage calculation module (21), a second BP neural network module (22) and a suspension force winding flux linkage amplitude phase observation module (23), the output ends of the second Clark conversion module (20) and the second voltage calculation module (21) are connected with the input end of the second BP neural network module (22), the output end of the second BP neural network module (22) is connected with the input end of the suspension force winding flux linkage amplitude phase observation module (23), and the input of the second Clark conversion module (20) is the suspension force winding stator phase current i2a、i2b、i2cStator phase current i of suspension force winding2a、i2b、i2cObtaining a current component i under a two-phase static coordinate system after Clark conversion2α、i2βThe input of the second voltage calculation module (21) is a suspension force winding direct-current power supply voltage U2DCAnd a switching signal S2a、S2b、S2cD.c. power supply voltage U of levitation winding2DCAnd a switching signal S2a、S2b、S2cThe voltage component u under the two-phase static coordinate system is obtained through calculation2α、u2βA current component i2αAnd a voltage component u2αFirst-order delay current obtained by first-order delay
Figure FDA0002525636820000022
And first order delay voltage
Figure FDA0002525636820000023
The second BP neural network module (22) outputs a flux linkage component psi of the suspension force winding flux linkage on the two-phase static coordinate system as an input of the second BP neural network module (22)s2α、ψs2βFlux linkage component psi of the levitation force windings2α、ψs2βThe amplitude phi phase of the suspension force winding flux linkage is output by the suspension force winding flux linkage amplitude phase calculation module (23) as the input of the suspension force winding flux linkage amplitude phase calculation module (23)s2And a phase λ.
4. The bearingless permanent magnet synchronous motor controller based on the flux linkage observer as claimed in claim 3, wherein: current component i2α、i2βAnd a voltage component u2α、u2βIs composed of
Figure FDA0002525636820000024
And formula
Figure FDA0002525636820000031
Obtaining; suspension force winding flux linkage amplitude phase calculation module (23) free form
Figure FDA0002525636820000032
Obtaining the amplitude psi of the magnetic linkage of the suspension force windings2And a phase λ.
5. The bearingless permanent magnet synchronous motor controller based on the flux linkage observer as claimed in claim 1 or 3, wherein: the neural network is a three-layer BP neural network with 6 inputs and 2 outputs.
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