CN109412492B - Speed loop control parameter self-tuning method based on fuzzy equivalent input interference method - Google Patents

Speed loop control parameter self-tuning method based on fuzzy equivalent input interference method Download PDF

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CN109412492B
CN109412492B CN201811427733.8A CN201811427733A CN109412492B CN 109412492 B CN109412492 B CN 109412492B CN 201811427733 A CN201811427733 A CN 201811427733A CN 109412492 B CN109412492 B CN 109412492B
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CN109412492A (en
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佘锦华
吴敏
刘振焘
张传科
李丹云
李美柳
蒋若愚
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China University of Geosciences
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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
    • H02P23/00—Arrangements or methods for the control of AC motors characterised by a control method other than vector control
    • H02P23/0004—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control
    • H02P23/0013—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
    • H02P23/00—Arrangements or methods for the control of AC motors characterised by a control method other than vector control
    • H02P23/14—Estimation or adaptation of motor parameters, e.g. rotor time constant, flux, speed, current or voltage

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Abstract

The invention discloses a speed loop control parameter self-tuning method based on a fuzzy equivalent input interference method, which aims at the problem of performance reduction and even instability of a servo control system caused by load and working condition changes, determines main parameters reflecting control performance in servo system model description through correlation analysis, and establishes a functional relation between control parameters to be tuned and system parameters of a permanent magnet synchronous motor; aiming at the problem that the control parameters of the speed ring of the servo system do not realize self-setting or have low self-setting efficiency, the comprehensive evaluation index of the position deviation of the servo system when load sudden change, mechanical vibration and external disturbance occur is combined, the fuzzy decision method is applied to execute the speed ring PI parameter self-setting, the equivalent input interference method realizes disturbance suppression, and the problem that the self-setting speed and the disturbance suppression effect are difficult to take account is solved.

Description

Speed loop control parameter self-tuning method based on fuzzy equivalent input interference method
Technical Field
The invention relates to the field of servo motors, in particular to a speed loop control parameter self-tuning method based on a fuzzy equivalent input interference method.
Background
With the rapid development of the industrial control industry, the demand for the motor control system is increasingly expanded. The method breaks through key technologies of networking, modularization, intellectualization, safety, high efficiency, energy conservation and the like of a servo system, develops high-quality series servo motors and driving products required by the industrial robot, has the functions of automatic inertia identification and automatic control parameter setting, and has very great economic and social benefits.
At present, the parameter setting work of the controller is mainly divided into two types: manual adjustment and automatic adjustment; the parameters of the controller are manually adjusted for many times by observing the rule of the running state of the system under different parameters, so that the method is time-consuming and labor-consuming, the effect is poor, and the requirement of the parameter adjusting work on operators is high. The automatic adjustment method is a rigid coefficient table set based on empirical values, and the table is automatically looked up through the change value of the load inertia ratio parameter so as to achieve the purpose of controlling the self-tuning of the parameter; under the condition that external disturbance changes rapidly, the method has the advantages of unobvious improvement on system performance and low reliability.
The southeast university discloses a patent with the name of CN 106877769A, namely a method for self-tuning gain parameters of a servo motor speed controller, namely the patent of the university of southeast, and a patent with the name of CN 101989827A, namely the method for self-tuning control parameters of an alternating current servo system speed ring based on inertia identification, namely the university of southeast. According to the method, the response speed is high, but the gain value is obtained on the basis of an empirical method and is mainly suitable for a system which is basically linear and has a dynamic characteristic which does not change along with time. And secondly, forming a servo system control parameter self-setting method based on inertia identification based on the relation between real-time inertia and PI control parameters, and estimating a value to be set of the PI parameter by establishing a corresponding functional relation between the rotational inertia and the PI control parameters. The method has simple design principle and high response speed, but depends on inertia identification precision and the accuracy of a built function, and is difficult to improve the interference suppression characteristic of the system.
Disclosure of Invention
The invention aims to solve the technical problems that in the prior art, aiming at the technical defects that the performance of a servo control system is reduced and even unstable due to load and working condition changes and the speed loop control parameters of the servo system do not realize self-setting or have low self-setting efficiency, the invention provides a speed loop control parameter self-setting method based on a fuzzy equivalent input interference method.
Aiming at the problem of performance reduction and even instability of a servo control system caused by load and working condition changes, main parameters reflecting control performance in servo system model description are determined through correlation analysis, and a functional relation between control parameters to be set and permanent magnet synchronous motor system parameters is established.
Aiming at the problem that the control parameters of the speed ring of the servo system do not realize self-setting or have low self-setting efficiency, the comprehensive evaluation index of the position deviation of the servo system when load sudden change, mechanical vibration and external disturbance occur is combined, the fuzzy decision method is applied to execute the speed ring PI parameter self-setting, the equivalent input interference method realizes disturbance suppression, and the problem that the self-setting speed and the disturbance suppression effect are difficult to take account is solved.
The invention provides a speed loop control parameter self-tuning method based on a fuzzy equivalent input interference method, which is used for acquiring control parameters with good quality in real time, reducing output deviation and improving tuning efficiency; in the traditional method, the disturbance phenomenon that the load continuously changes is not considered, the invention gives consideration to the fact that the load inertia mutation is the execution of the self-tuning of the system parameters, and can obtain better control effect.
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The invention will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a flow chart of an embodiment of a method for self-tuning a speed loop control parameter based on a fuzzy equivalent input disturbance method of the present invention;
FIG. 2 is a diagram of a parameter self-tuning design of the present invention;
fig. 3 is a fuzzy PI self-tuning structure diagram.
Detailed Description
For a more clear understanding of the technical features, objects and effects of the present invention, embodiments of the present invention will now be described in detail with reference to the accompanying drawings.
Referring to fig. 1, it is a flowchart of an embodiment of a speed loop control parameter self-tuning method based on a fuzzy equivalent input interference method according to the present invention, and the self-tuning method of the present embodiment includes the following steps.
S1, establishing a fuzzy PI controller, a state feedback controller, an EID controller, a state observer and a permanent magnet synchronous motor space equation of the servo system.
The mechanical equation of the permanent magnet synchronous motor comprises rotational inertia, as shown in formula one:
Figure GDA0002624659120000031
(formula one: mechanical equation of permanent magnet synchronous motor)
In the formula: j-moment of inertia; t ise-an electromagnetic torque; t isl-a load torque; b-coefficient of viscous friction; w-rotor angular velocity.
Establishing a permanent magnet synchronous motor vector model on the basis of the formula I, as shown in the formula II:
Figure GDA0002624659120000032
(formula two: vector equation of permanent magnet synchronous motor)
In the formula: r is armature winding resistance; l is the equivalent armature inductance; n isp-number of pole pairs of the motor; psif-a rotor flux linkage; b ist-magnetic induction. i.e. id，udCurrent and voltage for the d-axis; i.e. iq，uqCurrent and voltage of the q-axis.
Let x (t) be [ i ]diqw]T,y(t)＝[w]T,u(t)＝[uduq]T,d(t)＝TlAnd the equivalent model of the permanent magnet synchronous motor is shown as a formula III:
Figure GDA0002624659120000033
(equation III: permanent magnet synchronous machine equivalent equation)
In the formula
Figure GDA0002624659120000034
C＝[0 0 1]。
The invention is suitable for the control parameter self-tuning of a permanent magnet synchronous servo motor with speed change, and the parameter self-tuning design scheme is shown in figure 2 and mainly comprises a fuzzy PI controller, a state feedback controller, an equivalent input interference (EID) estimator, a permanent magnet synchronous motor space equation and a state observer. Wherein the reference definitions in fig. 2 are explained in a unified way as follows: r (t) denotes the rotor angular velocity w (t), KpProportional gain parameters of the PI controller; Δ KpFuzzy adjusting output quantity for proportional gain; x is the number ofp(t) is the value of the output error after proportional gain; kiIntegrating gain parameters for the PI controller; Δ KiAdjusting the output for integral gain ambiguity; x is the number ofi(t) is the value of the output error after integral gain; x is the number ofpi(t) is the output value of the error passing through the PI controller; kRAnd KFIs a state feedback controller gain value; f(s) is a low-pass filter, the state space expression of which is AF、BFAnd CFRepresents;
Figure GDA0002624659120000035
is a disturbance estimated value;
Figure GDA0002624659120000036
the disturbance estimation value is a value after filtering; l isRIs the observer gain; A. b, C is the permanent magnet synchronous motor system state variable, B+Is the pseudo-inverse of matrix B; u. off(t) is the output value of the state feedback controller; u (t) is a system input variable; x (t) is a system state variable; y (t) is a system output variable; d (t) is input perturbation; b isdPerturb the gain matrix for the input;
Figure GDA0002624659120000041
is the derivative of the system state variable;
Figure GDA0002624659120000042
is an observed value of a state variable;
Figure GDA0002624659120000043
as observations of output variablesThe value is obtained.
In the third formula, d (t) is the load torque/disturbance input of the permanent magnet synchronous motor control system. At the control input, a control input signal d is presente(t) the influence on the output is exactly the same as d (t), and is called de(t) is the equivalent input interference of interference input d (t). The state space (permanent magnet synchronous motor space equation) of the permanent magnet synchronous motor based on the equivalent input interference method is described as shown in formula four:
Figure GDA0002624659120000044
(formula four: permanent magnet synchronous motor model based on equivalent input interference method)
Suppose that the adjustment Δ K of the PI controller parameter at this timep＝0,ΔKi0; setting an initial value of a PI controller according to a nameplate of the permanent magnet synchronous motor, wherein the state space expression of the PI controller is shown as a formula V:
Figure GDA0002624659120000045
(formula five: PI controller formula)
In order to make the controlled object reconfigurable, a full-dimensional state observer is used, as shown in formula six:
Figure GDA0002624659120000046
(formula six: state observer)
Wherein the content of the first and second substances,
Figure GDA0002624659120000047
is an observed value of x (t), LRWhen the system time delay is known, the method can be used for observing the state of the controlled object.
The system state feedback control rate is designed as shown in formula seven:
Figure GDA0002624659120000048
(formula seven: system state feedback control rate)
The estimated value of the equivalent input interference is shown in formula eight:
Figure GDA0002624659120000049
(equation eight: equivalent input interference estimate)
Wherein, B+＝(BTB)-1BT。
Since the output y (t) contains noise, the disturbance is estimated using a low-pass filter with state space parameters AF、BFAnd CFThe filter description is as shown in equation nine:
Figure GDA0002624659120000051
(formula nine: State space description of Filter)
Wherein the content of the first and second substances,
Figure GDA0002624659120000052
for the disturbance signal after filtering, xF(t) is a state variable, and the transfer function of the filter needs to satisfy the following conditions:
Figure GDA0002624659120000053
thus, the closed loop system control input based on the equivalent input disturbance is
Figure GDA0002624659120000054
And S2, obtaining and setting gains of the state feedback controller and the state observer according to the fuzzy PI controller, the state feedback controller, the EID controller, the state observer and the permanent magnet synchronous motor space equation established in the step (A), so that the servo system is stable.
First, let the input signal and the external disturbance both be 0, i.e.: r (t) 0, d (t) 0, and
Figure GDA0002624659120000055
system for obtaining state error
Figure GDA0002624659120000056
Combining the formula four to nine, combining the state observer system, the state error system, the filter system and the PI control system, and using
Figure GDA0002624659120000057
To represent a resultant closed loop system, the state feedback control rate is expressed as:
Figure GDA0002624659120000058
wherein
Figure GDA0002624659120000059
The standard form of the system is finally simplified and is shown as formula ten:
Figure GDA00026246591200000510
(formula ten: closed loop system integral equation)
Wherein
Figure GDA00026246591200000511
Establishing a system stability condition according to an energy function, and designing an L-K functional V (t, x)t)＝xT(t) Px (t) and deriving to obtain
Figure GDA00026246591200000512
Wherein P ═ PT> 0 and is an arbitrary matrix. Order to
Figure GDA00026246591200000513
Wherein, P1,P2,P3,P4Respectively to be undetermined symmetrical positive real matrix, and then let Xi＝Pi -1I is 1,2,3,4, wherein X is diag { α X ═ X1X2X3βX4H, mixing V (t, x)t) Is multiplied by the block matrix X, and then, will
Figure GDA00026246591200000514
Substituting the value of (c) to obtain an LMI parameter expression phi < 0, and obtaining the LMI parameter expression phi as shown in a formula eleven.
Figure GDA0002624659120000061
(formula eleven: LMI parameter expression)
Wherein X2The singular value of (a) is decomposed into: x2＝Vdiag{X11,X22}VTSingular value decomposition of C to C ═ U [ S,0 ═]VTThe adjustable factors α and β are constants, and are generally set to 1, and an optimal value can be selected according to the output error performance index.
Substituting the actual parameters of the permanent magnet synchronous motor equation to obtain a value of A, B; setting an initial value K of a PI controllerpAnd Ki(ii) a Setting a low-pass filter parameter A according to the disturbance frequency characteristicF、BFAnd CFA value of (d); setting a positive definite symmetric matrix X to be solved1,X11,X22,X3,X4,W1,W2,W3Is defined by solving a feasible solution W of the following formula1,X1,W3,X4,W2,U,S,X11。
KF＝W1X1 -1,KR＝W3X4 -1,L＝W2USX11 -1S-1UT
(formula twelve: gain expression of state feedback controller and state observer)
And S3, fuzzifying and defuzzifying the error change rule of the rotor angular speed and the actual output angular speed according to the error change rule of the permanent magnet synchronous motor to obtain a PI control parameter fuzzy self-tuning parameter, and substituting the PI control parameter fuzzy self-tuning parameter into the fuzzy PI controller of S2 to realize self-tuning.
On the basis of the mathematical model, a second step is establishedThe dimension fuzzy control system is shown in fig. 3, wherein the permanent magnet synchronous motor in fig. 3 refers to the other part except the fuzzy PI controller in fig. 2. The deviation e of the rotating speed and the deviation rate ec of the rotating speed are used as input quantities of a controller to carry out fuzzification and fuzzy reasoning to obtain a fuzzy change quantity delta Kp，ΔKiFinally, the real-time adjustment K is obtained by adding the initial value of the PI controller shown in the figure 1p，KiTo obtain the optimal control parameters.
The blur amount E, EC corresponding to the deviation e of the rotation speed and the rotation speed deviation rate ec is used as an input amount, delta Kp，ΔKiTo blur the output, α is the feedback control gain. And (2) selecting 7 grades with alpha being 1, establishing a triangular membership function of the fuzzy set, and adjusting the amplitude according to the grade change corresponding to the PI control parameter according to the deviation to realize fuzzy inference.
In order to avoid the fuzzy inference from being trapped in local optimization, a control parameter optimization algorithm of a differential neural network is adopted to optimize a triangular membership function of a fuzzy set. And discretizing the continuous data, wherein the error expression is e (k). Aiming at the input of the neural network, respectively aiming at the radial basis function central point coordinate vector c of the ith neuron of the hidden layeriThe width b of the radial basis function of the ith neuron in the hidden layeriAnd a scale coefficient is added, so that the mapping capability of the neural network on the input can be quickly adjusted. Then for the output, respectively for Δ Kp、ΔKiAdding scale factor eta Kp、ηKiThis allows for a fast adjustment of the system's ability to map the output. These 6 parameters are encoded as real numbers as the content of the first part optimization.
ci＝nci*rands(i,j)
bi＝nbi*rands(i,j)
ΔKp＝ηKp*e(k)*dy
ΔKi＝ηKi*e(k)*dy
(thirteen formula: input Domain optimization of differential neural network)
Wherein, rands (i, j) is an array composed of random numbers i and j, n represents the number of output nodes of the neural network, dy represents the change rate of the output of the neural network, and e (k) represents the deviation of the rotating speed when the time sequence is k;
then, an error absolute value integration criterion is adopted as a part of an evaluation function of a difference algorithm, in order to prevent the instability of the system caused by overlarge control energy, a square integral term of control input is added into the evaluation function, in order to prevent overshoot, an integral term of overshoot ec is added, and therefore a fuzzy PI membership function is formed:
Figure GDA0002624659120000071
wherein w1And w2For the weight parameter preset according to the empirical method, it is possible to take w1＝1，w2＝0.001；
According to the obtained fuzzy PI membership function, the gravity center method is adopted to solve the fuzzy, and a new PI control parameter K is obtainedp+ΔKpAnd Ki+ΔKiAnd substituting the step II to perform disturbance suppression again, as shown in FIG. 2, so as to obtain a better control result.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.

Claims (3)

1. A speed loop control parameter self-tuning method based on a fuzzy equivalent input interference method is characterized by comprising the following steps:
s1, establishing a fuzzy PI controller, a state feedback controller, an EID controller, a state observer and a permanent magnet synchronous motor space equation of the servo system;
s2, obtaining and setting gains of the state feedback controller and the state observer according to the fuzzy PI controller, the state feedback controller, the EID controller, the state observer and the permanent magnet synchronous motor space equation established in the step (A), so that the servo system is stable;
s3, fuzzifying and defuzzifying the error change rule of the rotor angular speed and the actual output angular speed according to the error change rule of the permanent magnet synchronous motor to obtain a PI control parameter fuzzy self-tuning parameter, and substituting the PI control parameter fuzzy self-tuning parameter into the fuzzy PI controller of S2 to realize self-tuning;
in step S1, the space equation of the permanent magnet synchronous motor is:
Figure FDA0002624659110000011
the full-dimensional state observer is:
Figure FDA0002624659110000012
the state feedback controller is as follows:
Figure FDA0002624659110000013
the EID controller comprises:
Figure FDA0002624659110000014
the state equation of the filter in the EID controller is as follows:
Figure FDA0002624659110000015
wherein J represents the moment of inertia, w represents the rotor angular velocity, R represents the armature winding resistance, L represents the equivalent armature inductance, npRepresenting the number of pole pairs, ψ of the motorfThe flux linkage of the rotor is shown,
Figure FDA0002624659110000016
de(t) is the equivalent input interference of interference input d (t), KRAnd KFFor the gain value of the state feedback controller, AF、BFAnd CFTo representThe low-pass filter spatial state parameter,
Figure FDA0002624659110000021
in order to perturb the estimated value,
Figure FDA0002624659110000022
is a filtered value of the disturbance estimation value, LRTo observer gain, B+Is a pseudo-inverse of matrix B, B+＝(BTB)-1BT，uf(t) is the output value of the state feedback controller, u (t) is the system input variable, x (t) is the system state variable, and y (t) is the system output variable;
Figure FDA0002624659110000023
is a derivative of a state variable of the system,
Figure FDA0002624659110000024
is an observed value of the state variable,
Figure FDA0002624659110000025
in order to output the observed value of the variable,
Figure FDA0002624659110000026
is an observed value of x (t), BtDenotes magnetic induction, xFBeing state variables of filters, xpi(t) is the output value of the error passing through the fuzzy PI controller;
in step S2, the calculation of the gains of the state feedback controller and the state observer specifically includes the following steps:
s21, obtaining a value A, B according to the actual parameters of the permanent magnet synchronous motor equation;
s22, setting the initial value of the fuzzy PI controller to set KpAnd KiAnd setting a low-pass filter parameter AF、BFAnd CFA value of (d);
s23, using LMI toolbox of MATLAB, setting positive definite symmetric matrix X to be solved1,X11,X22,X3,X4,W1,W2,W3Is defined by solving a feasible solution W of the following formula1,X1,W3,X4,W2,U,S,X11；
Figure FDA0002624659110000027
Wherein X2The singular value of (a) is decomposed into: x2＝Vdiag{X11,X22}VTSingular value decomposition of C to C ═ U [ S,0 ═]VTThe adjustable factors alpha and beta are constants;
s24, obtaining and setting state feedback controller and observer gain K according to the following formulaF、KRAnd LR；
KF＝W1X1 -1,KR＝W3X4 -1,L＝W2USX11 -1S-1UT。
2. The speed loop control parameter self-tuning method based on the fuzzy equivalent input disturbance method according to claim 1, wherein the step S3 specifically comprises: the deviation e of the rotor angular speed and the rotating speed deviation rate ec are used as input quantities of a controller to carry out fuzzification and fuzzy reasoning to obtain a fuzzy change quantity delta Kp、ΔKiFinally adding the initial value of the fuzzy PI controller to adjust K in real timep、KiObtaining the optimal control parameter; Δ KpAdjusting the output, Δ K, for proportional gain ambiguityiThe output is dimmed for integral gain.
3. The speed loop control parameter self-tuning method based on the fuzzy equivalent input disturbance method according to claim 2, wherein the step S3 specifically comprises:
the blur amount E, EC corresponding to the deviation e of the rotation speed and the rotation speed deviation rate ec is used as an input amount, delta Kp，ΔKiTo blur the output, α is the feedback control gain, α ═1, establishing a triangular membership function of a fuzzy set, and adjusting the amplitude according to grade change corresponding to PI control parameters according to the deviation to realize fuzzy inference;
in order to avoid the fuzzy inference from being trapped in local optimization, a control parameter optimization algorithm of a differential neural network is adopted to optimize a triangular membership function of a fuzzy set, continuous data is discretized, an error expression is e (k), and a radial basis function central point coordinate vector c of the ith neuron of the hidden layer is respectively subjected to input of the neural networkiThe width b of the radial basis function of the ith neuron in the hidden layeriAdding scale factor to adjust the mapping ability of the neural network to the input, and then respectively mapping to delta K for the outputp、ΔKiAdding scale factor eta Kp、ηKiTo quickly adjust the mapping capability of the system to the output:
Figure FDA0002624659110000031
wherein, rands (i, j) is an array composed of random numbers i and j, n represents the number of output nodes of the neural network, dy represents the change rate of the output of the neural network, and e (k) represents the deviation of the rotating speed when the time sequence is k;
then, an error absolute value integration criterion is adopted as a part of an evaluation function of a difference algorithm, in order to prevent the instability of the system caused by overlarge control energy, a square integral term of control input is added into the evaluation function, in order to prevent overshoot, an integral term of overshoot ec is added, and therefore a fuzzy PI membership function is formed:
Figure FDA0002624659110000032
wherein w1And w2Is a weight parameter preset according to an empirical method;
according to the obtained fuzzy PI membership function, resolving the fuzzy by adopting a gravity center method, and obtaining a new fuzzy PI control parameter K by resolving the fuzzyp+ΔKpAnd Ki+ΔKiSubstituting into step S2, disturbance suppression is performed again to obtain a better control result.
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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20020021103A1 (en) * 2000-05-26 2002-02-21 Giuseppe Grasso Method and system for driving switched reluctance motors (SRM)
US20120056602A1 (en) * 2010-08-25 2012-03-08 Shuhui Li Control of a permanent magnet synchronous generator wind turbine
CN107450322A (en) * 2017-09-04 2017-12-08 中国地质大学（武汉） The stick slip vibration suppressing method and system of DRILL-STRING SYSTEM based on neutral model

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20020021103A1 (en) * 2000-05-26 2002-02-21 Giuseppe Grasso Method and system for driving switched reluctance motors (SRM)
US20120056602A1 (en) * 2010-08-25 2012-03-08 Shuhui Li Control of a permanent magnet synchronous generator wind turbine
CN107450322A (en) * 2017-09-04 2017-12-08 中国地质大学（武汉） The stick slip vibration suppressing method and system of DRILL-STRING SYSTEM based on neutral model

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
基于改进型等价输入干扰估计器的永磁同步电机速度控制;常军等;《2009中国控制与决策会议论文集（3）》;20090617;第5338-5343页 *
基于等价输入干扰补偿的几类典型***扰动抑制设计;刘瑞娟;《中国博士学位论文全文数据库》;20141215;第32-47页 *

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