CN110752807A - Speed-sensorless control system of bearingless asynchronous motor - Google Patents

Speed-sensorless control system of bearingless asynchronous motor Download PDF

Info

Publication number
CN110752807A
CN110752807A CN201910871254.3A CN201910871254A CN110752807A CN 110752807 A CN110752807 A CN 110752807A CN 201910871254 A CN201910871254 A CN 201910871254A CN 110752807 A CN110752807 A CN 110752807A
Authority
CN
China
Prior art keywords
neural network
current
control rate
observation
sliding mode
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN201910871254.3A
Other languages
Chinese (zh)
Inventor
王丁
杨泽斌
孙晓东
吴家杰
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Jiangsu University
Original Assignee
Jiangsu University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Jiangsu University filed Critical Jiangsu University
Priority to CN201910871254.3A priority Critical patent/CN110752807A/en
Publication of CN110752807A publication Critical patent/CN110752807A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/13—Observer control, e.g. using Luenberger observers or Kalman filters
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/0003—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control
    • H02P21/0007—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control using sliding mode control
    • 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
    • H02P21/05—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation specially adapted for damping motor oscillations, e.g. for reducing hunting

Landscapes

  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Control Of Ac Motors In General (AREA)
  • Control Of Electric Motors In General (AREA)

Abstract

The invention discloses a bearing-free asynchronous motor speed-sensorless control system, which comprises a Clark conversion module, an optimal control rate calculation system and an observation system, wherein the optimal control rate calculation system comprises a fractional order sliding mode control rate calculation module and a neural network compensation term calculation module; according to the invention, the fractional order sliding mode theory is combined with the neural network through the optimal control rate calculation system, so that the observation shake is reduced, the robustness of the system to parameter time variation and external disturbance is enhanced, and the speed-sensor-free control of the bearingless asynchronous motor with good observation performance is realized.

Description

Speed-sensorless control system of bearingless asynchronous motor
Technical Field
The invention belongs to the technical field of electric transmission control equipment, and particularly relates to a speed sensorless control system of a bearingless asynchronous motor based on a joint neural network and a fractional order sliding mode.
Background
With the development of social productivity, the application of the motor is wider and wider, and the requirement is higher and higher. Compared with the common motor, the bearingless motor is a novel motor with the characteristics of no friction, no abrasion, no need of lubrication, high speed, super high speed and the like. The bearingless asynchronous motor has the advantages of simple and firm structure, low manufacturing cost, uniform air gap, stable cogging torque, good flux weakening speed regulation and the like, and has special application value in the fields of flywheel energy storage, chemical engineering chemistry, life science, transportation and the like.
With the deep research of the bearingless asynchronous motor, the control theory of the bearingless asynchronous motor is rapidly developed. The torque control part of the motor cannot be used for detecting and feeding back the rotating speed, but the traditional mechanical speed sensor is easy to be influenced by the environment due to friction, so that the rotating speed observation accuracy of the motor is low, and the control accuracy of the motor is seriously influenced. Therefore, the realization of the online observation of the rotating speed of the motor becomes a necessary premise for ensuring the accurate operation of the motor control system.
At present, the rotating speed of the bearingless asynchronous motor is observed on line by various methods. Chinese patent application No. CN201710153450.8, entitled: a speed sensorless control method for a bearingless asynchronous motor adopts a method of current injection to construct a rotor position deviation angle, and achieves the purpose of motor rotation speed estimation by adjusting the deviation angle through PI. However, the high-frequency signal introduced by the method is easily doped with other high-frequency signals, so that a control system is complicated. Chinese patent application No. CN201210124559.6, entitled: a construction method of a speed sensorless system of a bearingless asynchronous motor adopts an inverse algorithm of a support vector machine to construct the speed sensorless system of the bearingless asynchronous motor. However, the method is complex and the engineering is difficult to implement. Chinese patent application No. CN201110003563.2, entitled: a speed sensor-free construction method for detecting the rotating speed of a bearing-free asynchronous motor adopts a neural network inverse algorithm to realize the observation of the speed of the bearing-free asynchronous motor. However, the differential estimation algorithm based on the backward difference in the strategy has a large differential estimation error under a noise environment. Therefore, the three methods have the problems that the control structure is complex and other interference items are easily introduced, so that the observation precision of the rotating speed of the motor is indirectly influenced, and the difficulty of the motor in practical application is increased.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a speed sensorless control system of a bearingless asynchronous motor, which can realize accurate and rapid detection control over the whole range of the rotating speed of the bearingless asynchronous motor, and can reduce the influence of parameter time variation and load disturbance on motor control, so that the whole motor has good control performance and does not influence the suspension characteristic.
The technical scheme adopted by the invention is as follows:
a bearing-free asynchronous motor speed sensorless control system comprises a Clark conversion module, an optimal control rate calculation system and an observation system,
the observation system comprises an observation current flux linkage calculation module and a rotating speed solving module;
the optimal control rate calculation system comprises a fractional order sliding mode control rate calculation module and a neural network compensation item calculation module;
the difference value of the two-phase current converted and output by the Clark conversion module and the observation current of the observation current flux linkage calculation module is respectively input into the fractional order sliding mode control rate calculation module and the neural network compensation term calculation module, the difference value output by the fractional order sliding mode control rate calculation module and the neural network compensation term calculation module is input into the observation current flux linkage calculation module, the output of the observation current flux linkage calculation module is input into the rotating speed solving module, and the rotating speed solving module outputs the final observation rotating speed.
Further, the construction method of the optimal control rate calculation system is as follows:
s1, obtaining an expression of the observation current and the observation flux linkage according to the mathematical model of the bearingless asynchronous motor;
s2, selecting a fractional order sliding mode to switch the popular surface by taking the current error as input, and calculating the fractional order sliding mode control rate by utilizing a fractional order sliding mode control theory and a Lyapunoy theory;
s3, approximating the optimal form of the fractional order sliding mode control rate by combining the neural network principle, designing an adaptive law by utilizing the stability requirement of the Lyapunoy theory, and finally realizing the design of the optimal control rate.
Further, the expressions of the observed flux linkage and the observed current are as follows:
Figure BDA0002202881350000021
Figure BDA0002202881350000022
wherein the content of the first and second substances,
Figure BDA0002202881350000023
the first derivatives, rho, of the components of the observed flux linkage in the α and β axes, respectivelyα、ρβControl rates in the α and β axial directions,
Figure BDA0002202881350000024
are respectively as
Figure BDA0002202881350000025
The first derivative of (a) is,
Figure BDA0002202881350000026
the observed current components, u, on the α and β axes, respectivelysα、usβThe stator voltage components, k, on the α and β axes, respectively1＝Lm/(σLsLr)，k2＝Rs/(σLs)，k3＝1/(σLs)，LmFor mutual inductance of windings, LrFor self-inductance of the rotor, LsIn order to realize the self-inductance of the stator,
Figure BDA0002202881350000027
is the magnetic flux leakage coefficient.
Further, the fractional order sliding mode control rate calculation process is as follows: according to current observation errors:
Figure BDA0002202881350000028
designing a switching current surface of a fractional order sliding mode:
Figure BDA0002202881350000029
selecting a Lyapunov function: l is1＝1/2s2And finally, obtaining the fractional order sliding mode control rate as follows:
Figure BDA0002202881350000031
wherein e isα、eβFor α, β axis current observation errors,
Figure BDA00022028813500000311
are each eα、eβThe first derivative of the signal is a derivative of,
Figure BDA0002202881350000033
is the first derivative of s, D is the fractional calculus operator, α is the order of the fractional sliding mode, c1、c2、μ0Is a normal number.
Further, the process of S3 is:
s3.1, selecting a neural network approximation algorithm as follows:
Figure BDA0002202881350000034
f＝W*Th (x) + epsilon, input defining the neural network as x ═ e, de]The output is obtained as
Figure BDA0002202881350000035
Then the approximate optimal control rate that can be solved is
Figure BDA0002202881350000036
Wherein h isjIs a Gaussian basis function of the neural network, x is the neural network input, cijFor the neural network hidden layer center vector, bjIs a vector of the base width of the network,for control rate error, e is current error, de is first derivative of current error, i represents input number of neural network, j represents j node of hidden layer of neural network, h ═ hj]TRepresenting the output of a Gaussian function, W*Representing the ideal weight of the neural network, and epsilon representing the approximation error of the neural network
S3.2, selecting a Lyapunov function:
Figure BDA0002202881350000038
and (3) deriving the function, substituting the optimal control rate, and obtaining the self-adaptive rate by utilizing the stability condition:and finally, solving the optimal control rate.
Further, the rotating speed solving module calculates an observed value of the rotating speed solving module according to the current and flux linkage equation, and substitutes the observed value into the rotating speed equation:
Figure BDA00022028813500000310
and obtaining a final rotating speed observed value.
The invention has the beneficial effects that:
1. the sensorless control technology of the bearingless asynchronous motor combining the neural network and the fractional order sliding mode can accurately and quickly realize the observation of the rotating speed of the motor, thereby eliminating the defects of inaccurate observation, easy environmental influence and the like of the traditional mechanical speed sensor and improving the control precision of the bearingless asynchronous motor.
2. The optimal control rate constructed by the method is combined with the traditional sliding mode through the fractional order theory, so that the shaking problem of the traditional sliding mode is reduced, the optimal control rate is obtained by combining the approximation capability of the neural network, the influence of sign functions, parameter time variation and load disturbance on rotating speed observation is inhibited, and the robustness and the dynamic performance of the observer are enhanced.
3. The speed sensorless control technology of the bearingless asynchronous motor combining the neural network and the fractional order sliding mode saves the volume of the bearingless asynchronous motor, promotes the change of the bearingless asynchronous motor to the direction of miniaturization and practicability, and simultaneously reduces the control cost of the motor.
Drawings
FIG. 1 is a block diagram of a bearingless asynchronous motor speed sensorless control system of the present invention;
in the figure, 1, a Clark transformation module, 2, an optimal control rate calculation system, 3, an observation system, 21, a fractional order sliding mode control rate calculation module, 22, a neural network compensation term calculation module, 31, an observation current magnetic linkage calculation module, 32 and a rotating speed solving module.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Because the control of the traditional mechanical speed sensor is inaccurate, is easily influenced by the environment and the like, the control precision of the motor torque part is reduced, so that in order to eliminate the defects of the traditional mechanical speed sensor and improve the observation precision of the bearingless asynchronous motor, the bearingless asynchronous motor speed sensor control system combining the neural network and the fractional order sliding mode, as shown in figure 1, comprises a Clark conversion module 1, an optimal control rate calculation system 2 and an observation system 3; the observation system 3 comprises an observation current flux linkage calculation module 31 and a rotating speed solving module 32; the optimal control rate calculation system 2 includes a fractional order sliding mode control rate calculation module 21 and a neural network compensation term calculation module 22.
Three-phase current i1abcAnd voltage u1abcConverted into a static coordinate system by a Clark conversion module 1 to obtain a two-phase current i1αβVoltage u1αβThe obtained current i1αβThe observation current fed back by the observation current flux linkage calculation module 31
Figure BDA0002202881350000041
Performing difference to obtain an error e, and inputting the error e into a fractional sliding mode control rate calculation 21 and a neural network compensation calculation 22 respectively to obtain a control rate rho*And compensation termControl rate ρ*And compensation term
Figure BDA0002202881350000043
And obtaining the optimal control rate rho by difference. The optimal control rates rho and u are calculated1αβThe current flux and flux are input into the observed current flux and flux calculation module 31 together, and the observed values of the current and flux are obtained by the observed current flux and flux calculation module 31 respectively
Figure BDA0002202881350000044
And
Figure BDA0002202881350000045
then the observed value is measured
Figure BDA0002202881350000046
And
Figure BDA0002202881350000047
and inputting the rotation speed to a rotation speed solving module 32 to finally obtain the observed rotation speed of the bearing-free asynchronous motor.
The optimal control rate calculation system 2 is constructed by the following steps:
s1, analyzing a mathematical model of the torque module of the bearingless asynchronous motor, and converting the simplified variables to obtain expressions of the observed current and the observed flux linkage:
Figure BDA0002202881350000048
wherein the content of the first and second substances,
Figure BDA0002202881350000051
the first derivatives, rho, of the components of the observed flux linkage in the α and β axes, respectivelyα、ρβControl rates in the α and β axial directions,
Figure BDA0002202881350000052
are respectively as
Figure BDA0002202881350000053
The first derivative of (a) is,
Figure BDA0002202881350000054
the observed current components, u, on the α and β axes, respectivelysα、usβThe stator voltage components, k, on the α and β axes, respectively1＝Lm/(σLsLr)，k2＝Rs/(σLs)，k3＝1/(σLs)，LmFor mutual inductance of windings, LrFor self-inductance of the rotor, LsIn order to realize the self-inductance of the stator,
Figure BDA0002202881350000055
is the magnetic flux leakage coefficient.
S2, selecting a fractional order sliding mode to switch the popular surface by taking the current error e as input, designing the fractional order sliding mode control rate by utilizing a fractional order sliding mode control theory and a Lyapunoy theory to improve the robustness of the observer, and then proving the stability of the control rate, wherein the process is as follows:
s2.1, according to current observation errors:
Figure BDA0002202881350000056
designing a switching current surface of a fractional order sliding mode:
Figure BDA0002202881350000057
the first derivative is obtained by solving s:selecting a Lyapunov function: l is1＝1/2s2Then, then
Figure BDA0002202881350000059
Taking mu0∈R+Satisfy mu0＞max(|c1k1M|，|c1k1N |), and s ≦ s × sign(s), then:
Figure BDA00022028813500000510
therefore, the fractional order sliding mode control rate can be selected as follows:
Figure BDA00022028813500000511
wherein e isα、eβFor α, β axis current observation errors,
Figure BDA00022028813500000516
are each eα、eβThe first derivative of the signal is a derivative of,
Figure BDA00022028813500000513
is the first derivative of s, D is the fractional calculus operator, α is the order of the fractional sliding mode, c1、c2、μ0Is constant and c1∈R+，c2∈R+，μ0∈R+，
Figure BDA00022028813500000514
S2.2, substituting the fractional order sliding mode control rate into the Lyapunov function to obtain the fractional order sliding mode control rate
Figure BDA00022028813500000515
The selected control rate is proved to meet the stable condition.
S3, approximating the optimal form of the fractional order sliding mode control rate by combining a neural network principle, and designing an adaptive law by utilizing the stability requirement of the Lyapunoy theory so as to reduce the influence of sign function, parameter time variation, external disturbance and the like on the observation precision and finally realize the design of the optimal control rate; s3.1, adopting a neural network to approach the optimal control rate as follows:
Figure BDA0002202881350000061
the approximation algorithm selected is as follows:
Figure BDA0002202881350000062
f＝W*Th (x) + ε, according to input x ═ e, de of neural network]The output is obtained as
Figure BDA0002202881350000063
Then the approximate optimal control rate that can be solved is
Figure BDA0002202881350000064
Wherein h isjIs a Gaussian basis function of the neural network, x is the neural network input, cijFor the neural network hidden layer center vector, bjIs a vector of the base width of the network,
Figure BDA0002202881350000065
for control rate error, e is current error, de is first derivative of current error, i represents input number of neural network, j represents j node of hidden layer of neural network, h ═ hj]TRepresenting the output of a Gaussian function, W*Representing an ideal weight of the neural network, wherein epsilon represents an approximation error of the neural network;
s3.2, according to
Figure BDA0002202881350000066
Can be solved to
Figure BDA0002202881350000067
Figure BDA0002202881350000068
Wherein the content of the first and second substances,
Figure BDA0002202881350000069
defining:
Figure BDA00022028813500000610
then a new first derivative of s can be obtained as:
Figure BDA00022028813500000611
selecting a Lyapunov function:the function is derived and the new first derivative of s is substituted to give:
Figure BDA00022028813500000613
to make it possible to
Figure BDA00022028813500000614
The adaptive rate can be selected as follows:
Figure BDA00022028813500000615
and finally, solving the optimal control rate.
The observed current/flux linkage calculation module 31 obtains the expressions of observed current and flux linkage to obtain the observed values of current and flux linkage, and then the output of the observed current/flux linkage calculation module 31 is substituted into the speed solving module 32
Figure BDA00022028813500000616
And (5) equation to obtain the final observed rotating speed. The method effectively saves the space of the bearingless asynchronous motor, promotes the conversion of the bearingless asynchronous motor to the miniaturization and practicability direction, and simultaneously reduces the control cost of the motor.
The above embodiments are only used for illustrating the design idea and features of the present invention, and the purpose of the present invention is to enable those skilled in the art to understand the content of the present invention and implement the present invention accordingly, and the protection scope of the present invention is not limited to the above embodiments. Therefore, all equivalent changes and modifications made in accordance with the principles and concepts disclosed herein are intended to be included within the scope of the present invention.

Claims (6)

1. A speed sensor-free control system of a bearingless asynchronous motor is characterized by comprising a Clark transformation module (1), an optimal control rate calculation system (2) and an observation system (3), wherein the observation system (3) comprises an observation current magnetic linkage calculation module (31) and a rotating speed solving module (32); the optimal control rate calculation system 2 comprises a fractional order sliding mode control rate calculation module (21) and a neural network compensation term calculation module (22); the difference value of the two-phase current converted and output by the Clark conversion module 1 and the observation current of the observation current flux linkage calculation module (31) is respectively input into a fractional order sliding mode control rate calculation module (21) and a neural network compensation term calculation module (22), the difference value output by the fractional order sliding mode control rate calculation module (21) and the neural network compensation term calculation module (22) is input into the observation current flux linkage calculation module (31), the output of the observation current flux linkage calculation module (31) is input into a rotating speed solving module (32), and the rotating speed solving module (32) outputs the final observation rotating speed.
2. The bearingless asynchronous motor speed sensorless control system according to claim 1, characterized in that the optimal control rate calculation system (2) is constructed by:
s1, obtaining an expression of the observation current and the observation flux linkage according to the mathematical model of the bearingless asynchronous motor;
s2, selecting a fractional order sliding mode to switch the popular surface by taking the current error as input, and calculating the fractional order sliding mode control rate by utilizing a fractional order sliding mode control theory and a Lyapunoy theory;
s3, approximating the optimal form of the fractional order sliding mode control rate by combining the neural network principle, designing an adaptive law by utilizing the stability requirement of the Lyapunoy theory, and finally realizing the design of the optimal control rate.
3. The bearingless asynchronous motor speed sensorless control system of claim 2 wherein the expressions of observed flux linkage and observed current are:
Figure FDA0002202881340000011
wherein the content of the first and second substances,the first derivatives, rho, of the components of the observed flux linkage in the α and β axes, respectivelyα、ρβControl rates in the α and β axial directions,
Figure FDA0002202881340000013
are respectively as
Figure FDA0002202881340000014
The first derivative of (a) is,
Figure FDA0002202881340000015
the observed current components, u, on the α and β axes, respectivelysα、usβThe stator voltage components, k, on the α and β axes, respectively1＝Lm/(σLsLr)，k2＝Rs/(σLs)，k3＝1/(σLs)，LmFor mutual inductance of windings, LrFor self-inductance of the rotor, LsIn order to realize the self-inductance of the stator,
Figure FDA0002202881340000016
is the magnetic flux leakage coefficient.
4. The speed sensorless control system of the bearingless asynchronous motor according to claim 2, wherein the fractional order sliding mode control rate calculation process is: according to current observation errors:
Figure FDA0002202881340000017
designing a switching current surface of a fractional order sliding mode:
Figure FDA0002202881340000021
selecting a Lyapunov function: l is1＝1/2s2And finally, obtaining the fractional order sliding mode control rate as follows:
Figure FDA0002202881340000022
wherein e isα、eβFor α, β axis current observation errors,
Figure FDA0002202881340000023
are each eα、eβThe first derivative of the signal is a derivative of,is the first derivative of s, D is the fractional calculus operator, α is the order of the fractional sliding mode, c1、c2、μ0Is a normal number.
5. The bearingless asynchronous motor speed sensorless control system according to claim 2, wherein the process of S3 is:
s3.1, selecting a neural network approximation algorithm as follows:
Figure FDA0002202881340000025
f＝W*Th (x) + epsilon, input defining the neural network as x ═ e, de]The output is obtained as
Figure FDA0002202881340000026
Then the approximate optimal control rate that can be solved is
Figure FDA0002202881340000027
Wherein h isjIs a Gaussian basis function of the neural network, x is the neural network input, cijFor the neural network hidden layer center vector, bjIs a vector of the base width of the network,
Figure FDA0002202881340000028
for control rate error, e is current error, de is first derivative of current error, i represents input number of neural network, j represents j node of hidden layer of neural network, h ═ hj]TRepresenting the output of a Gaussian function, W*Representing the ideal weight of the neural network, and epsilon representing the approximation error of the neural network
S3.2, selecting a Lyapunov function:
Figure FDA0002202881340000029
and (3) deriving the function, substituting the optimal control rate, and obtaining the self-adaptive rate by utilizing the stability condition:
Figure FDA00022028813400000210
and finally, solving the optimal control rate.
6. The sensorless control system of a bearingless asynchronous motor according to claim 1, wherein the rotation speed solving module (32) calculates the observed value according to the current and flux linkage equation, and substitutes the observed value into the rotation speedThe velocity equation:
Figure FDA00022028813400000211
and obtaining a final rotating speed observed value.
CN201910871254.3A 2019-09-16 2019-09-16 Speed-sensorless control system of bearingless asynchronous motor Pending CN110752807A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201910871254.3A CN110752807A (en) 2019-09-16 2019-09-16 Speed-sensorless control system of bearingless asynchronous motor

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201910871254.3A CN110752807A (en) 2019-09-16 2019-09-16 Speed-sensorless control system of bearingless asynchronous motor

Publications (1)

Publication Number Publication Date
CN110752807A true CN110752807A (en) 2020-02-04

Family

ID=69276417

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201910871254.3A Pending CN110752807A (en) 2019-09-16 2019-09-16 Speed-sensorless control system of bearingless asynchronous motor

Country Status (1)

Country Link
CN (1) CN110752807A (en)

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106374488A (en) * 2016-09-13 2017-02-01 河海大学常州校区 Fractional order terminal sliding mode-based AFNN control method of active power filter
CN107147120A (en) * 2017-06-29 2017-09-08 河海大学常州校区 Active Power Filter-APF RBF amphineura network adaptive sliding-mode observer methods
CN108712119A (en) * 2018-04-09 2018-10-26 江苏大学 Induction-type bearingless motor anti-disturbance control system based on sliding moding structure

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106374488A (en) * 2016-09-13 2017-02-01 河海大学常州校区 Fractional order terminal sliding mode-based AFNN control method of active power filter
CN107147120A (en) * 2017-06-29 2017-09-08 河海大学常州校区 Active Power Filter-APF RBF amphineura network adaptive sliding-mode observer methods
CN108712119A (en) * 2018-04-09 2018-10-26 江苏大学 Induction-type bearingless motor anti-disturbance control system based on sliding moding structure

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
SYUAN-YI CHEN等: "Precision Motion Control of Permanent Magnet", 《IEEE》 *
WEIMING SUN等: "Speed Sensorless of a Bearingless Induction", 《IEEE》 *
巨梅: "基于分数阶滑模转速估计的感应电机控制研究", 《中国优秀硕士学位论文全文数据库》 *
张碧陶等: "集成神经网络与自适应算法的分数阶滑模控制", 《控制理论与应用》 *

Similar Documents

Publication Publication Date Title
CN110429881B (en) Active-disturbance-rejection control method of permanent magnet synchronous motor
CN102035456B (en) Direct torque control system of permanent magnet synchronous motor based on terminal sliding mode
CN111600523B (en) Model prediction current control method of permanent magnet synchronous motor
CN112731805B (en) Wind power generator maximum power tracking sensorless robust control method based on wind speed estimation
CN111211717B (en) IPMSM (intelligent power management system) position-sensorless motor closed-loop control method of nonsingular sliding mode structure
CN110165953B (en) PMSM speed regulation control method based on approximation law
CN111692210B (en) Construction method of active magnetic bearing controller based on table look-up method
CN113206623A (en) Permanent magnet synchronous motor finite time speed regulation control method based on fast integral terminal sliding mode and interference estimation
CN110131312B (en) Five-degree-of-freedom alternating current active magnetic bearing active disturbance rejection decoupling controller and construction method
CN111293947A (en) Improved speed sensorless control method for permanent magnet synchronous motor
CN110752807A (en) Speed-sensorless control system of bearingless asynchronous motor
CN112003523A (en) Method for improving speed estimation stability of permanent magnet synchronous linear motor
CN113395028B (en) Method for forming flux linkage observer based on voltage and current hybrid model
CN116317771A (en) Position-sensor-free control method for low-speed and high-speed switching of permanent magnet synchronous motor
CN115001334A (en) Rotation speed control method and system of position-sensor-free ultra-high-speed permanent magnet synchronous motor based on active disturbance rejection
CN113691179B (en) Permanent magnet synchronous motor control method based on fixed time power-varying exponent approach law
CN109687798A (en) Mix stator magnetic suspension switched reluctance motor suspension system high_order sliding mode control method
Tsai et al. Implementation of a DSP‐based speed‐sensorless adaptive control for permanent‐magnet synchronous motor drives with uncertain parameters using linear matrix inequality approach
CN114710080A (en) Permanent magnet synchronous motor sliding mode control method based on improved variable gain approximation law
CN114527650A (en) Vibration compensation method for magnetic bearing rotor of variable-step LMS adaptive filter
CN113489408A (en) Permanent magnet linear motor speed sensorless control system
CN111884556A (en) Finite time repetitive control method for induction motor
Zhao et al. Design of MRAC and Modified MRAC for the Turntable
Yang et al. Decoupling control for bearingless synchronous reluctance motor based on support vector machine inverse
CN112152528B (en) Permanent magnet synchronous motor speed regulation control method based on self-adaptive terminal sliding mode

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
RJ01 Rejection of invention patent application after publication

Application publication date: 20200204

RJ01 Rejection of invention patent application after publication