CN110492809B - Asynchronous motor dynamic surface discrete fault-tolerant control method based on neural network approximation - Google Patents

Asynchronous motor dynamic surface discrete fault-tolerant control method based on neural network approximation Download PDF

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CN110492809B
CN110492809B CN201910788111.6A CN201910788111A CN110492809B CN 110492809 B CN110492809 B CN 110492809B CN 201910788111 A CN201910788111 A CN 201910788111A CN 110492809 B CN110492809 B CN 110492809B
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asynchronous motor
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CN110492809A (en
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于金鹏
田新诚
雷启鑫
张国斌
胡成江
王博
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Qingdao 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
    • H02P21/06—Rotor flux based control involving the use of rotor position or rotor speed sensors
    • 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
    • H02P25/00—Arrangements or methods for the control of AC motors characterised by the kind of AC motor or by structural details
    • H02P25/02—Arrangements or methods for the control of AC motors characterised by the kind of AC motor or by structural details characterised by the kind of motor
    • 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
    • H02P27/00—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage
    • H02P27/04—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage using variable-frequency supply voltage, e.g. inverter or converter supply voltage
    • H02P27/06—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage using variable-frequency supply voltage, e.g. inverter or converter supply voltage using dc to ac converters or inverters
    • H02P27/08—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage using variable-frequency supply voltage, e.g. inverter or converter supply voltage using dc to ac converters or inverters with pulse width modulation
    • 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
    • H02P2207/01—Asynchronous machines

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Abstract

The invention belongs to the technical field of asynchronous motor position tracking control, and particularly discloses a neural network approximation-based asynchronous motor dynamic surface discrete fault-tolerant control method. Aiming at the problem of actuator faults which are easy to occur in an asynchronous motor driving system, the method establishes an actuator fault model and simultaneously establishes a discrete fault model of the asynchronous motor system by combining an Euler method; the dynamic surface control technology is introduced into the traditional backstepping method, so that the problems of 'calculation explosion' and 'cause and effect contradiction' in a backstepping control algorithm of a discrete system are solved; the method utilizes a radial basis function neural network to process nonlinear items in an asynchronous motor discrete system, solves the problems of unknown parameters and actuator faults in the system by combining an adaptive control method, and constructs an asynchronous motor dynamic surface discrete fault-tolerant controller based on neural network approximation; the method can overcome the influence of actuator faults, ensure an ideal control effect and realize quick and stable response to the rotating speed.

Description

Asynchronous motor dynamic surface discrete fault-tolerant control method based on neural network approximation
Technical Field
The invention belongs to the technical field of asynchronous motor position tracking control, and particularly relates to a dynamic surface discrete fault-tolerant control method for an asynchronous motor based on neural network approximation.
Background
Asynchronous motors (IMs) are alternating current motors, also called induction motors, and are mainly used as motors. Asynchronous motors are widely used in industrial and agricultural production, for example: asynchronous motors are used as prime movers for beds, water pumps, metallurgical, mining equipment, and light industrial machinery. However, the mathematical model of the asynchronous motor has the characteristics of high nonlinearity, strong coupling, multivariable and the like, and is easily influenced by uncertain factors such as motor parameter variation and external load disturbance, so that it is a challenging task to realize high-performance control of the asynchronous motor.
In recent years, the research of nonlinear control methods, such as sliding mode control, dynamic surface control, hamiltonian control, backstepping control and other control methods, has made great progress. However, most of these techniques are based on asynchronous motor continuous systems, for which there are fewer control algorithms for discrete systems. Because the actual engineering system mostly adopts the discrete control technology, and the discrete control algorithm is superior to the continuous algorithm in realizability and stability. Therefore, the control method for the discrete system construction of the asynchronous motor has very important practical significance. In addition, the above control methods do not consider the effect of actuator failure during the operation of the asynchronous motor. In the running process of the asynchronous motor, as the asynchronous motor continuously executes control tasks for a long time, the possibility of system execution mechanism failure is increased, once the system execution mechanism fails and is not processed in time, the performance of the control system is reduced, and even serious damage is caused to equipment and personal safety.
The backstepping method is an effective nonlinear system control method, and the core thought is to decompose a complex nonlinear system into a plurality of simple low-order subsystems, design the controller step by introducing a virtual control function, and finally determine a real control law, so that the high-order nonlinear system is effectively controlled. However, when the back-stepping method is applied to a discrete system, the continuous differentiation process of the virtual control function can cause the problems of 'computational explosion' and 'causal contradiction'. In addition, for some high-order systems with high nonlinearity and uncertain parameters, the nonlinear term can cause the controller to become very complex, thus the burden of online calculation is increased, the online control of a computer control system is not facilitated, and the traditional backstepping method is difficult to deal with the problem of uncertain parameters.
Disclosure of Invention
The invention aims to provide a dynamic surface discrete fault-tolerant control method of an asynchronous motor based on neural network approximation, which comprehensively considers the problem of actuator faults easily occurring in the operation of the motor and realizes the rapid and stable position tracking control of the asynchronous motor.
In order to achieve the purpose, the invention adopts the following technical scheme:
the asynchronous motor dynamic surface discrete fault-tolerant control method based on neural network approximation comprises the following steps:
a. establishing discrete dynamic mathematical model of asynchronous motor
Under a synchronous rotating coordinate system, oriented according to the rotor flux linkage, the asynchronous motor driving system model can be expressed as:
Figure GDA0002586816720000021
in the formula (I), the compound is shown in the specification,
Figure GDA0002586816720000022
nprepresenting the number of pole pairs, T, of an asynchronous motorLRepresenting load torque, J representing moment of inertia, LmRepresenting the mutual inductance, omega the rotor angular velocity, theta the rotor angle, psidRepresenting the rotor flux linkage; i.e. idRepresenting d-axis current, iqRepresenting the q-axis current, udDenotes the d-axis voltage, uqRepresents the q-axis voltage; rsRepresenting the resistance of the stator, LsRepresenting the inductance of the stator; rrRepresenting the resistance of the rotor, LrRepresenting the inductance of the rotor;
to simplify the discrete dynamic mathematical model of an asynchronous motor, new variables are defined as follows:
Figure GDA0002586816720000023
wherein k is the step number of the discrete system; theta (k), omega (k), iq(k)、ψd(k) And id(k) Respectively representing a rotor angle, a rotor angular speed, a q-axis current, a rotor flux linkage and a d-axis current corresponding to the k step;
the following two actuator fault models are defined respectively:
deviation fault model: u (k) ═ v (k) + b (k) (2)
Wherein u (k) represents the actual control input after the actuator failure, v (k) represents the control input given by the control method, and b (k) is a bounded function;
gain failure fault model: u (k) ═ 1- ρ v (k) (3)
In the formula, rho represents a gain failure parameter, and rho is more than or equal to 0 and less than 1;
the bias and gain failures can be expressed as follows: u (k) ═ 1- ρ v (k) + b (k) (4)
Obtaining a discrete fault model of the asynchronous motor by a new variable and an Euler formula, namely:
Figure GDA0002586816720000031
wherein, DeltatA sampling period representing a discrete system of asynchronous motors; v. ofq(k) And vd(k) Representing a true control law;
ρ1a gain failure parameter representing a q-axis stator voltage;
ρ2a gain failure parameter representing a d-axis stator voltage;
b1(k) a deviation fault function representing the q-axis stator voltage; b2(k) A deviation fault function representing the d-axis stator voltage;
b. according to the dynamic surface technology and the principle of a back-stepping method, an asynchronous motor dynamic surface discrete fault-tolerant control method based on neural network approximation is designed, and a discrete fault model of an asynchronous motor is simplified into two independent subsystems, namely:
by a state variable x1(k)，x2(k)，x3(k) And a control input vq(k) Formed subsystem and composed of state variables x4(k),x5(k) And controlInput vd(k) A component subsystem;
for a continuous non-linear function f (Z), there is a radial basis function neural network WTP (Z) is such that: f (Z) ═ WTP (Z) + tau (Z), wherein tau (Z) is an approximation error and satisfies | tau (Z) | less than or equal to any small normal number;
Figure GDA0002586816720000032
is an input vector, q is the neural network input dimension, RqA real number vector set;
W∈Rlis a weight vector, the number of nodes of the neural network is a positive integer, l is greater than 1, RlA real number vector set;
P(Z)＝[p1(Z),...,pl(Z)]T∈Rlis a vector of basis functions; p is a radical ofc(Z) is a Gaussian function, and the expression is as follows:
Figure GDA0002586816720000033
wherein, c is 1cIs the center of the receiving domain, ηcIs the width of the gaussian function;
defining the dynamic surface filter as:
Figure GDA0002586816720000041
wherein i is 1,2,3, ζiAs time constant of the filter, αi(k) For virtual control law, make αi(k) The output signal α of the dynamic surface filter is obtained by first-order low-pass filteringid(k)，αi(0) Representation αi(k) αid(0) Representation αid(k) Initial value of (c), virtual control law αi(k) The specific structure of (a) will be given in the following control method design;
the tracking error variables are defined as:
Figure GDA0002586816720000042
wherein x is1d(k) For the desired position signal, x4d(k) Is the desired rotor flux linkage signal;
in the control method, a Lyapunov function is selected in each step to construct a control function, and the specific process is as follows:
b.1. selecting a Lyapunov function:
Figure GDA0002586816720000043
to V1(k) And obtaining the difference:
Figure GDA0002586816720000044
selecting a virtual control function:
Figure GDA0002586816720000045
according to the error variable e2(k)＝x2(k)-α1d(k) And is obtained based on the young inequality:
Figure GDA0002586816720000046
b.2. selecting a Lyapunov function:
Figure GDA0002586816720000047
then to V2(k) And obtaining the difference:
Figure GDA0002586816720000048
load torque T in actual operation of asynchronous motorLIs a bounded value, set | TLL is less than or equal to d, wherein d is more than 0;
selecting a virtual control function:
Figure GDA0002586816720000049
according to the error variable e3(k)＝x3(k)-α2d(k) And is obtained based on the young inequality:
Figure GDA0002586816720000051
b.3. selecting a Lyapunov function:
Figure GDA0002586816720000052
to V3(k) And obtaining the difference:
Figure GDA0002586816720000053
Figure GDA0002586816720000054
from the principle of radial basis function neural network approximation, for a given arbitrary3> 0, a radial basis function neural network W is present3 TP3(z3(k) So that:
Figure GDA0002586816720000055
wherein, g3(k) Is an unknown non-linear function; z is a radical of3(k)＝[x1(k),x2(k),x3(k),x4(k),x5(k)]T，τ3Expressing approximation error and satisfying inequality | tau3|≤3，||W3Is the vector W3Thereby:
Figure GDA0002586816720000056
selecting a true control law vq(k) And law of adaptation
Figure GDA0002586816720000057
Comprises the following steps:
Figure GDA0002586816720000058
Figure GDA0002586816720000059
wherein, γ3And3is a normal number; definition | | | W3||＝η3And η3> 0, define variable η3Is estimated error of
Figure GDA00025868167200000510
Comprises the following steps:
Figure GDA00025868167200000511
Figure GDA00025868167200000512
is variable η3An estimated value of (d); formula (13) is substituted for formula (12) to obtain:
Figure GDA00025868167200000513
b.4. selecting a Lyapunov function:
Figure GDA00025868167200000514
to V4(k) And obtaining the difference:
Figure GDA00025868167200000515
selecting a virtual control function:
Figure GDA00025868167200000516
according to the error variable e5(k)＝x5(k)-α3d(k) And is obtained based on the young inequality:
Figure GDA0002586816720000061
b.5. selecting a Lyapunov function:
Figure GDA0002586816720000062
m > 0 is a constant, for V5(k) And obtaining the difference:
Figure GDA0002586816720000063
wherein the content of the first and second substances,
Figure GDA0002586816720000064
from the radial basis function neural network approximation theorem, it is known that for arbitrary5Not less than 0, a radial basis function neural network W exists5 TP5(z5(k) Make sure that
Figure GDA0002586816720000065
Wherein, g5(k) Is an unknown non-linear function; z is a radical of5(k)＝[x1(k),x2(k),x3(k),x4(k),x5(k)]T，τ5Represents an approximation error and satisfies | τ5|≤5，||W5Is the vector W5Thereby:
Figure GDA0002586816720000066
selecting a true control law vd(k) And law of adaptation
Figure GDA0002586816720000067
Comprises the following steps:
Figure GDA0002586816720000068
Figure GDA0002586816720000069
wherein, γ5And5is a positive number, and the number of the positive number,
Figure GDA00025868167200000610
is η5The estimated value of (1), define | | W5||＝η5And η5> 0, define variable η5Is estimated error of
Figure GDA00025868167200000611
Comprises the following steps:
Figure GDA00025868167200000612
substituting equation (20) into equation (19) yields:
Figure GDA00025868167200000613
substituting equations (8), (10), (15), and (17) into equation (22) yields:
Figure GDA00025868167200000614
Figure GDA0002586816720000071
c. carrying out stability analysis on the constructed asynchronous motor dynamic surface discrete fault-tolerant control method based on neural network approximation;
defining a filtering error mui(k) Comprises the following steps: mu.si(k)＝αid(k)-αi(k) I ═ 1,2,3, the Lyapunov function was chosen:
Figure GDA0002586816720000072
differentiating V (k) to obtain:
Figure GDA0002586816720000073
definition of vi(k)＝αi(k)-αi(k +1) obtained by the formula (6):
Figure GDA0002586816720000074
further obtaining:
Figure GDA0002586816720000075
namely:
Figure GDA0002586816720000076
according to
Figure GDA0002586816720000077
j ═ 3,5, and equation (14) yield:
Figure GDA0002586816720000078
defined by the radial basis function P (Z) | | P3(z3(k))||2＜l3，||P5(z5(k))||2＜l5，l3And l5Respectively representing a neural network W3 TP3(z3(k) ) and W5 TP5(z5(k) Node number of); based on the young inequality:
Figure GDA0002586816720000081
Figure GDA0002586816720000082
Figure GDA0002586816720000083
Figure GDA0002586816720000084
by an error variable e3(k)＝x3(k)-α2d(k)、e5(k)＝x5(k)-α3d(k) Formula (13) and formula (20):
Figure GDA0002586816720000085
Figure GDA0002586816720000086
Figure GDA0002586816720000087
Figure GDA0002586816720000088
substituting equations (29), (30), (31), (32), and (33) into equation (28) yields:
Figure GDA0002586816720000089
substituting equations (29), (30), (31), (32), and (34) into equation (28) yields:
Figure GDA00025868167200000810
during the operation of the motor, the rotor flux linkage is a bounded numerical value, thus defining
Figure GDA00025868167200000811
Where N is a normal number, the substitution of equations (22), (27), (35) and (36) into equation (25) yields:
Figure GDA00025868167200000812
Figure GDA0002586816720000091
wherein the content of the first and second substances,
Figure GDA0002586816720000092
selection of appropriate M and ΔtTo make inequality satisfy
Figure GDA0002586816720000093
Figure GDA0002586816720000094
The selected parameter satisfies
Figure GDA0002586816720000095
Error of the measurement
Figure GDA0002586816720000096
And
Figure GDA0002586816720000097
if true, obtaining that delta V (k) is less than or equal to 0;
further, it is known that for
Figure GDA0002586816720000098
If true;
the true control law v is obtained from the above analysisq(k) And vd(k) Under the action of (2), the tracking error e of the discrete system of the asynchronous motor1(k) And e4(k) It is possible to converge to a sufficiently small neighborhood of the origin and ensure that the other signals are bounded.
The invention has the following advantages:
(1) the method aims at a discrete time system, and has higher stability and realizability compared with a control method of a continuous time system.
(2) The method comprehensively considers the problems of gain failure and deviation of the actuator of the asynchronous motor, eliminates the influence of faults on the control system by utilizing a fault-tolerant control technology, ensures that the designed control system can still stably run after the faults occur, and effectively solves the problem of position tracking control of the asynchronous motor under the condition of the fault of the actuator.
(3) The method adopts the dynamic surface technology, so that the problems of 'calculation explosion' and 'cause and effect contradiction' caused by continuous difference solving of the virtual function in the traditional back step method are effectively avoided; the neural network is used for processing unknown nonlinear terms in the motor system, so that the problem of highly nonlinear control of the asynchronous motor is effectively solved, and finally, more accurate control precision is achieved.
(4) The method of the invention does not need to modify the parameters of the controller according to the difference of the asynchronous motors, can realize the stable control of the asynchronous motors with all models and powers in principle, reduces the measurement of the parameters of the asynchronous motors in the control process, and is beneficial to realizing the quick response of the rotation speed adjustment of the asynchronous motors.
Drawings
Fig. 1 is a schematic diagram of a composite controlled object composed of an asynchronous motor dynamic surface discrete fault-tolerant controller based on neural network approximation, a coordinate transformation unit and an SVPWM inverter in the embodiment of the invention;
FIG. 2 is a rotor angle and rotor angle set value tracking simulation diagram after the control method of the present invention is adopted;
FIG. 3 is a simulation diagram of the rotor angle and the set value tracking error of the rotor angle after the control method of the present invention is adopted;
FIG. 4 is a simulation diagram of rotor flux linkage and rotor flux linkage setpoint tracking after the control method of the present invention is employed;
FIG. 5 is a simulation diagram of the voltage of the stator of the d-axis of the asynchronous motor after the control method of the invention is adopted;
fig. 6 is a simulation diagram of the q-axis stator voltage of the asynchronous motor after the control method of the invention is adopted.
Detailed Description
The basic idea of the invention is as follows:
aiming at the problem of actuator faults which are easy to occur in an asynchronous motor driving system, an actuator fault model is established, and meanwhile, a discrete fault model of the asynchronous motor system is established by combining an Euler method; the dynamic surface control technology is introduced into the traditional back-stepping method, so that the problems of 'calculation explosion' and 'cause and effect contradiction' caused by continuous difference solving in a discrete system are solved; the nonlinear items in the discrete motor model are processed by utilizing the radial basis function neural network, the problems of unknown parameters and actuator faults in the system are solved by combining the self-adaptive control method, and the asynchronous motor dynamic surface discrete fault-tolerant controller based on neural network approximation is constructed.
The invention is described in further detail below with reference to the following figures and detailed description:
FIG. 1 is a schematic diagram of a composite controlled object composed of a neural network approximation-based asynchronous motor dynamic surface discrete fault-tolerant controller, a coordinate transformation unit and an SVPWM inverter in an embodiment of the present invention, where ω represents rotor angular velocity, U representsαAnd UβIndicating the voltage in the two-phase rotating coordinate system, and U, V and W indicating the three-phase ac voltage.
Referring to fig. 1, the asynchronous motor dynamic surface discrete fault-tolerant control method based on neural network approximation adopts components including an asynchronous motor dynamic surface discrete fault-tolerant controller 1 based on neural network approximation, a coordinate transformation unit 2, an SVPWM inverter 3, a rotation speed detection unit 4, and a current detection unit 5.
The rotating speed detection unit 4 and the current detection unit 5 are mainly used for detecting the current value and the rotating speed related variable of the asynchronous motor, the actually measured current and the actually measured rotating speed variable are used as input, voltage control is carried out through the asynchronous motor dynamic surface discrete fault-tolerant controller 1 based on neural network approximation, and finally the three-phase current and rotating speed related variable are converted into the rotating speed of the three-phase electrically controlled asynchronous motor.
In order to design a more efficient controller, it is necessary to build a dynamic model of the asynchronous motor.
The asynchronous motor dynamic surface discrete fault-tolerant control method based on neural network approximation comprises the following steps:
a. establishing discrete dynamic mathematical model of asynchronous motor
Oriented according to the rotor flux linkage (psi) in a synchronous rotating coordinate systemq0), the asynchronous motor drive system model can be expressed as:
Figure GDA0002586816720000111
in the formula (I), the compound is shown in the specification,
Figure GDA0002586816720000112
nprepresenting the number of pole pairs, T, of an asynchronous motorLRepresenting load torque, J representing moment of inertia, LmRepresenting the mutual inductance, omega the rotor angular velocity, theta the rotor angle, psidRepresenting the rotor flux linkage; i.e. idRepresenting d-axis current, iqRepresenting the q-axis current, udDenotes the d-axis voltage, uqRepresents the q-axis voltage; rsRepresenting the resistance of the stator, LsRepresenting the inductance of the stator; rrRepresenting the resistance of the rotor, LrRepresenting the inductance of the rotor.
To simplify the discrete dynamic mathematical model of an asynchronous motor, new variables are defined as follows:
Figure GDA0002586816720000113
wherein k is the step number of the discrete system; theta (k), omega (k), iq(k)、ψd(k) And id(k) And respectively representing the corresponding rotor angle, rotor angular speed, q-axis current, rotor flux linkage and d-axis current at the k step.
The actuator faults considered in the embodiment of the invention are deviation faults and gain faults, and the following two fault models are respectively defined:
deviation fault model: u (k) ═ v (k) + b (k) (2)
Where u (k) represents the actual control input after actuator failure, v (k) represents the control input given by the control method, and b (k) is a bounded function.
Gain failure fault model: u (k) ═ 1- ρ v (k) (3)
In the formula, rho represents a gain failure parameter, and rho is more than or equal to 0 and less than 1.
The bias and gain failures can be expressed as follows: u (k) ═ 1- ρ v (k) + b (k) (4)
Obtaining a discrete fault model of the asynchronous motor by a new variable and an Euler formula, namely:
Figure GDA0002586816720000121
wherein, DeltatA sampling period representing a discrete system of asynchronous motors; v. ofq(k) And vd(k) Representing the true control law.
ρ1A gain failure parameter representing the q-axis stator voltage.
ρ2A gain failure parameter representing the d-axis stator voltage.
b1(k) A deviation fault function representing the q-axis stator voltage; b2(k) A deviation fault function of the d-axis stator voltage is represented.
b. According to the dynamic surface technology and the principle of a back-stepping method, an asynchronous motor dynamic surface discrete fault-tolerant control method based on neural network approximation is designed, and a discrete fault model of an asynchronous motor is simplified into two independent subsystems, namely:
by a state variable x1(k)，x2(k)，x3(k) And a control input vq(k) Formed subsystem and composed of state variables x4(k),x5(k) And a control input vd(k) And (4) forming a subsystem.
For a continuous non-linear function f (Z), there is a radial basis function neural network WTP (Z) is such that: f (Z) ═ WTP (Z) + tau (Z), wherein tau (Z) is an approximation error and satisfies | tau (Z) | ≦ being any small normal number.
Figure GDA0002586816720000122
Is an input vector, q is the neural network input dimension, RqIs a set of real vectors.
W∈RlIs a weight vector, the number of nodes of the neural network is a positive integer, l is greater than 1, RlIs a set of real vectors.
P(Z)＝[p1(Z),...,pl(Z)]T∈RlIs a vector of basis functions. p is a radical ofc(Z) is a Gaussian function, and the expression is as follows:
Figure GDA0002586816720000123
wherein, c is 1cIs the center of the receiving domain, ηcIs the width of the gaussian function.
Defining the dynamic surface filter as:
Figure GDA0002586816720000131
wherein i is 1,2,3, ζiAs time constant of the filter, αi(k) For virtual control law, make αi(k) The output signal α of the dynamic surface filter is obtained by first-order low-pass filteringid(k)，αi(0) Representation αi(k) αid(0) Representation αid(k) Initial value of (c), virtual control law αi(k) The specific structure of (a) will be given in the following control method design.
The tracking error variables are defined as:
Figure GDA0002586816720000132
wherein x is1d(k) For the desired position signal, x4d(k) Is the desired rotor flux linkage signal.
In the control method, a Lyapunov function is selected in each step to construct a control function, and the specific process is as follows:
b.1. selecting a Lyapunov function:
Figure GDA0002586816720000133
to V1(k) And obtaining the difference:
Figure GDA0002586816720000134
selecting a virtual control function:
Figure GDA0002586816720000135
according to the error variable e2(k)＝x2(k)-α1d(k) And is obtained based on the young inequality:
Figure GDA0002586816720000136
b.2. selecting a Lyapunov function:
Figure GDA0002586816720000137
then to V2(k) And obtaining the difference:
Figure GDA0002586816720000138
load torque T in actual operation of asynchronous motorLIs a bounded value, set | TLL is less than or equal to d, wherein d is more than 0.
Selecting a virtual control function:
Figure GDA0002586816720000139
according to the error variable e3(k)＝x3(k)-α2d(k) And is obtained based on the young inequality:
Figure GDA0002586816720000141
b.3. selecting a Lyapunov function:
Figure GDA0002586816720000142
to V3(k) And obtaining the difference:
Figure GDA0002586816720000143
Figure GDA0002586816720000144
from the principle of radial basis function neural network approximation, for a given arbitrary3> 0, a radial basis function neural network W is present3 TP3(z3(k) So that:
Figure GDA0002586816720000145
wherein, g3(k) Is an unknown non-linear function; z is a radical of3(k)＝[x1(k),x2(k),x3(k),x4(k),x5(k)]T，τ3Expressing approximation error and satisfying inequality | tau3|≤3，||W3Is the vector W3Thereby:
Figure GDA0002586816720000146
selecting a true control law vq(k) And law of adaptation
Figure GDA0002586816720000147
Comprises the following steps:
Figure GDA0002586816720000148
Figure GDA0002586816720000149
wherein, γ3And3is a normal number; definition | | | W3||＝η3And η3> 0, define variable η3Is estimated error of
Figure GDA00025868167200001410
Comprises the following steps:
Figure GDA00025868167200001411
Figure GDA00025868167200001412
is variable η3An estimated value of (d); formula (13) is substituted for formula (12) to obtain:
Figure GDA00025868167200001413
b.4. selecting a Lyapunov function:
Figure GDA00025868167200001414
to V4(k) And obtaining the difference:
Figure GDA00025868167200001415
selecting a virtual control function:
Figure GDA00025868167200001416
according to the error variable e5(k)＝x5(k)-α3d(k) And is obtained based on the young inequality:
Figure GDA0002586816720000151
b.5. selecting a Lyapunov function:
Figure GDA0002586816720000152
m > 0 is a constant, for V5(k) And obtaining the difference:
Figure GDA0002586816720000153
wherein the content of the first and second substances,
Figure GDA0002586816720000154
from the radial basis function neural network approximation theorem, it is known that for arbitrary5Not less than 0, a radial basis function neural network W exists5 TP5(z5(k) Make sure that
Figure GDA0002586816720000155
Wherein, g5(k) Is an unknown non-linear function;z5(k)＝[x1(k),x2(k),x3(k),x4(k),x5(k)]T，τ5represents an approximation error and satisfies | τ5|≤5，||W5Is the vector W5Norm of (d). So that:
Figure GDA0002586816720000156
selecting a true control law vd(k) And law of adaptation
Figure GDA0002586816720000157
Comprises the following steps:
Figure GDA0002586816720000158
Figure GDA0002586816720000159
wherein, γ5And5is a positive number, and the number of the positive number,
Figure GDA00025868167200001510
is η5The estimated value of (1), define | | W5||＝η5And η5> 0, define variable η5Is estimated error of
Figure GDA00025868167200001511
Comprises the following steps:
Figure GDA00025868167200001512
substituting equation (20) into equation (19) yields:
Figure GDA00025868167200001513
substituting equations (8), (10), (15), and (17) into equation (22) yields:
Figure GDA00025868167200001514
Figure GDA0002586816720000161
c. and carrying out stability analysis on the constructed asynchronous motor dynamic surface discrete fault-tolerant control method based on neural network approximation.
Defining a filtering error mui(k) Comprises the following steps: mu.si(k)＝αid(k)-αi(k) I ═ 1,2,3, the Lyapunov function was chosen:
Figure GDA0002586816720000162
differentiating V (k) to obtain:
Figure GDA0002586816720000163
definition of vi(k)＝αi(k)-αi(k +1) obtained by the formula (6):
Figure GDA0002586816720000164
further obtaining:
Figure GDA0002586816720000165
namely:
Figure GDA0002586816720000166
according to
Figure GDA0002586816720000167
j ═ 3,5, and equation (14) yield:
Figure GDA0002586816720000168
defined by the radial basis function P (Z) | | P3(z3(k))||2＜l3，||P5(z5(k))||2＜l5，l3And l5Respectively representing a neural network W3 TP3(z3(k) ) and W5 TP5(z5(k) Node number of). Based on the young inequality:
Figure GDA0002586816720000171
Figure GDA0002586816720000172
Figure GDA0002586816720000173
Figure GDA0002586816720000174
by an error variable e3(k)＝x3(k)-α2d(k)、e5(k)＝x5(k)-α3d(k) Formula (13) and formula (20):
Figure GDA0002586816720000175
Figure GDA0002586816720000176
Figure GDA0002586816720000177
Figure GDA0002586816720000178
substituting equations (29), (30), (31), (32), and (33) into equation (28) yields:
Figure GDA0002586816720000179
substituting equations (29), (30), (31), (32), and (34) into equation (28) yields:
Figure GDA00025868167200001710
during the operation of the motor, the rotor flux linkage is a bounded numerical value, thus defining
Figure GDA00025868167200001711
Where N is a normal number, the substitution of equations (22), (27), (35) and (36) into equation (25) yields:
Figure GDA00025868167200001712
Figure GDA0002586816720000181
wherein the content of the first and second substances,
Figure GDA0002586816720000182
selection of appropriate M and ΔtTo make inequality satisfy
Figure GDA0002586816720000183
Figure GDA0002586816720000184
The selected parameter satisfies
Figure GDA0002586816720000185
Error of the measurement
Figure GDA0002586816720000186
And
Figure GDA0002586816720000187
if true, obtaining Δ V (k) is less than or equal to0。
Further, it is known that for
Figure GDA0002586816720000188
This is true.
The true control law v is obtained from the above analysisq(k) And vd(k) Under the action of (2), the tracking error e of the discrete system of the asynchronous motor1(k) And e4(k) It is possible to converge to a sufficiently small neighborhood of the origin and ensure that the other signals are bounded.
And simulating the established asynchronous motor dynamic surface discrete fault-tolerant control method based on neural network approximation in a virtual environment to verify the feasibility of the control method.
The motor and load parameters are:
J＝0.0586Kg·m2，Rs＝0.1Ω，Rr＝0.15Ω，Ls＝Lr＝0.0699H，Lm＝0.068H。
the tracking reference signal is: x is the number of1d(k)＝sin(Δtk pi/2); the expected rotor flux linkage signal is: x is the number of4d(k)＝1。
Sampling period: deltatThe load torque is 0.0025 s:
Figure GDA0002586816720000189
selecting the control law parameters as follows:
3＝1.25，5＝1.6，γ3＝0.175，γ5＝0.25，ζ1＝0.0012，ζ2＝0.00074，ζ3＝0.0012。
the radial basis function neural network is selected as follows: neural network W3 TP3(z3(k) ) and W5 TP5(z5(k) Contains 9 centers evenly distributed in [ -8,8 ]]The width of each internal node is 2.
Considering the failure of the gain of the actuator and the deviation fault of the actuator in the running process of the asynchronous motor, the fault conditions are as follows:
gain failure fault proportionality coefficient:
Figure GDA0002586816720000191
deviation fault function:
Figure GDA0002586816720000192
the corresponding simulation results are shown in fig. 2,3, 4, 5 and 6. Wherein:
fig. 2 and fig. 4 are a tracking simulation diagram of the rotor angular position and the set value of the rotor angular position and a tracking simulation diagram of the rotor flux linkage and the set value of the rotor flux linkage, respectively, after being controlled by the method of the present invention, and the simulation results show that:
under the condition that the actuator has a fault, the controller can respond quickly, so that the system can recover the ideal operation effect.
FIG. 3 is a graph showing a simulation of the tracking error of the rotor angular position and the rotor angular position set point after control by the method of the present invention. Fig. 5 and fig. 6 are simulation diagrams of voltages of a d-axis stator and a q-axis stator of an asynchronous motor respectively after being controlled by the method of the invention.
The analog signal clearly shows that the asynchronous motor dynamic surface discrete fault-tolerant control method based on neural network approximation can quickly and stably track the reference signal under the fault condition that the gain of an actuator fails and deviates.
It should be understood, however, that the description herein of specific embodiments is not intended to limit the invention to the particular forms disclosed, but on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the invention as defined by the appended claims.

Claims (1)

1. The asynchronous motor dynamic surface discrete fault-tolerant control method based on neural network approximation is characterized in that,
the method comprises the following steps:
a. establishing discrete dynamic mathematical model of asynchronous motor
Under a synchronous rotating coordinate system, oriented according to the rotor flux linkage, the asynchronous motor driving system model can be expressed as:
Figure FDA0002586816710000011
in the formula (I), the compound is shown in the specification,
Figure FDA0002586816710000012
nprepresenting the number of pole pairs, T, of an asynchronous motorLRepresenting load torque, J representing moment of inertia, LmRepresenting the mutual inductance, omega the rotor angular velocity, theta the rotor angle, psidRepresenting the rotor flux linkage; i.e. idRepresenting d-axis current, iqRepresenting the q-axis current, udDenotes the d-axis voltage, uqRepresents the q-axis voltage; rsRepresenting the resistance of the stator, LsRepresenting the inductance of the stator; rrRepresenting the resistance of the rotor, LrRepresenting the inductance of the rotor;
to simplify the discrete dynamic mathematical model of an asynchronous motor, new variables are defined as follows:
Figure FDA0002586816710000013
wherein k is the step number of the discrete system, theta (k), omega (k), iq(k)、ψd(k) And id(k) Respectively representing a rotor angle, a rotor angular speed, a q-axis current, a rotor flux linkage and a d-axis current corresponding to the k step;
the following two actuator fault models are defined respectively:
deviation fault model: u (k) ═ v (k) + b (k) (2)
Wherein u (k) represents the actual control input after the actuator failure, v (k) represents the control input given by the control method, and b (k) is a bounded function;
gain failure fault model: u (k) ═ 1- ρ v (k) (3)
In the formula, rho represents a gain failure parameter, and rho is more than or equal to 0 and less than 1;
the bias and gain failures can be expressed as follows: u (k) ═ 1- ρ v (k) + b (k) (4)
Obtaining a discrete fault model of the asynchronous motor by a new variable and an Euler formula, namely:
Figure FDA0002586816710000021
wherein, DeltatA sampling period representing a discrete system of asynchronous motors;
vq(k) and vd(k) Representing a true control law;
ρ1gain failure parameter, ρ, representing the q-axis stator voltage2A gain failure parameter representing a d-axis stator voltage;
b1(k) representing the deviation fault function of the q-axis stator voltage, b2(k) A deviation fault function representing the d-axis stator voltage;
b. according to the dynamic surface technology and the principle of a back-stepping method, an asynchronous motor dynamic surface discrete fault-tolerant control method based on neural network approximation is designed, and a discrete fault model of an asynchronous motor is simplified into two independent subsystems, namely:
by a state variable x1(k)，x2(k)，x3(k) And a control input vq(k) Formed subsystem and composed of state variables x4(k),x5(k) And a control input vd(k) A component subsystem;
for a continuous non-linear function f (Z), there is a radial basis function neural network WTP (Z) is such that: f (Z) ═ WTP (Z) + tau (Z), wherein tau (Z) is an approximation error and satisfies | tau (Z) | less than or equal to any small normal number;
Figure FDA0002586816710000022
is an input vector, q is the neural network input dimension, RqA real number vector set;
W∈Rlis a weight vector, the number of nodes of the neural network is a positive integer, l is greater than 1, RlA real number vector set;
P(Z)＝[p1(Z),...,pl(Z)]T∈Rlis a vector of basis functions; p is a radical ofc(Z) is a Gaussian function, and the expression is as follows:
Figure FDA0002586816710000031
wherein, c is 1cIs the center of the receiving domain, ηcIs the width of the gaussian function;
defining the dynamic surface filter as:
Figure FDA0002586816710000032
wherein i is 1,2,3, ζiAs time constant of the filter, αi(k) For virtual control law, make αi(k) The output signal α of the dynamic surface filter is obtained by first-order low-pass filteringid(k)，αi(0) Representation αi(k) αid(0) Representation αid(k) Initial value of (c), virtual control law αi(k) The specific structure of (a) will be given in the following control method design;
the tracking error variables are defined as:
Figure FDA0002586816710000033
wherein x is1d(k) For the desired position signal, x4d(k) Is the desired rotor flux linkage signal;
in the control method, a Lyapunov function is selected in each step to construct a control function, and the specific process is as follows:
b.1. selecting a Lyapunov function:
Figure FDA0002586816710000034
to V1(k) And obtaining the difference:
Figure FDA0002586816710000035
selecting a virtual control function:
Figure FDA0002586816710000036
according to the error variable e2(k)＝x2(k)-α1d(k) And is obtained based on the young inequality:
Figure FDA0002586816710000037
b.2. selecting a Lyapunov function:
Figure FDA0002586816710000038
then to V2(k) And obtaining the difference:
Figure FDA0002586816710000039
load torque T in actual operation of asynchronous motorLIs a bounded value, set | TLL is less than or equal to d, wherein d is more than 0;
selecting a virtual control function:
Figure FDA0002586816710000041
according to the error variable e3(k)＝x3(k)-α2d(k) And is obtained based on the young inequality:
Figure FDA0002586816710000042
b.3. selecting a Lyapunov function:
Figure FDA0002586816710000043
to V3(k) And obtaining the difference:
Figure FDA0002586816710000044
Figure FDA0002586816710000045
from the principle of radial basis function neural network approximation, for a given arbitrary3> 0, radial basis function neural networks exist
Figure FDA0002586816710000046
Such that:
Figure FDA0002586816710000047
wherein, g3(k) Is an unknown non-linear function; z is a radical of3(k)＝[x1(k),x2(k),x3(k),x4(k),x5(k)]T，τ3Expressing approximation error and satisfying inequality | tau3|≤3，||W3Is the vector W3Thereby:
Figure FDA0002586816710000048
selecting a true control law vq(k) And law of adaptation
Figure FDA0002586816710000049
Comprises the following steps:
Figure FDA00025868167100000410
Figure FDA00025868167100000411
wherein, γ3And3is a normal number; definition | | | W3||＝η3And η3> 0, define variable η3Is estimated error of
Figure FDA00025868167100000412
Comprises the following steps:
Figure FDA00025868167100000413
Figure FDA00025868167100000414
is variable η3An estimated value of (d); formula (13) is substituted for formula (12) to obtain:
Figure FDA00025868167100000415
b.4. selecting a Lyapunov function:
Figure FDA00025868167100000416
to V4(k) And obtaining the difference:
Figure FDA00025868167100000417
selecting a virtual control function:
Figure FDA0002586816710000051
according to the error variable e5(k)＝x5(k)-α3d(k) And is obtained based on the young inequality:
Figure FDA0002586816710000052
b.5. selecting a Lyapunov function:
Figure FDA0002586816710000053
m > 0 is a constant, for V5(k) And obtaining the difference:
Figure FDA0002586816710000054
wherein the content of the first and second substances,
Figure FDA0002586816710000055
from the radial basis function neural network approximation theorem, it is known that for arbitrary5> 0, presence of radial basis function neural networks
Figure FDA0002586816710000056
Make it
Figure FDA0002586816710000057
Wherein, g5(k) Is an unknown non-linear function; z is a radical of5(k)＝[x1(k),x2(k),x3(k),x4(k),x5(k)]T，τ5Represents an approximation error and satisfies | τ5|≤5，||W5Is the vector W5Thereby:
Figure FDA0002586816710000058
selecting a true control law vd(k) And law of adaptation
Figure FDA0002586816710000059
Comprises the following steps:
Figure FDA00025868167100000510
Figure FDA00025868167100000511
wherein, γ5And5is a positive number, and the number of the positive number,
Figure FDA00025868167100000512
is η5The estimated value of (1), define | | W5||＝η5And η5> 0, define variable η5Is estimated error of
Figure FDA00025868167100000513
Comprises the following steps:
Figure FDA00025868167100000514
substituting equation (20) into equation (19) yields:
Figure FDA00025868167100000515
substituting equations (8), (10), (15), and (17) into equation (22) yields:
Figure FDA0002586816710000061
c. carrying out stability analysis on the constructed asynchronous motor dynamic surface discrete fault-tolerant control method based on neural network approximation;
defining a filtering error mui(k) Comprises the following steps: mu.si(k)＝αid(k)-αi(k) I ═ 1,2,3, the Lyapunov function was chosen:
Figure FDA0002586816710000062
differentiating V (k) to obtain:
Figure FDA0002586816710000063
definition of vi(k)＝αi(k)-αi(k +1) obtained by the formula (6):
Figure FDA0002586816710000064
further obtaining:
Figure FDA0002586816710000065
namely:
Figure FDA0002586816710000066
according to
Figure FDA0002586816710000067
j ═ 3,5, and equation (14) yield:
Figure FDA0002586816710000071
defined by the radial basis function P (Z) | | P3(z3(k))||2＜l3，||P5(z5(k))||2＜l5，l3And l5Respectively representing neural networks
Figure FDA0002586816710000072
And
Figure FDA0002586816710000073
the number of nodes of (a); based on the young inequality:
Figure FDA0002586816710000074
Figure FDA0002586816710000075
Figure FDA0002586816710000076
Figure FDA0002586816710000077
by an error variable e3(k)＝x3(k)-α2d(k)、e5(k)＝x5(k)-α3d(k) Formula (13) and formula (20):
Figure FDA0002586816710000078
Figure FDA0002586816710000079
Figure FDA00025868167100000710
Figure FDA00025868167100000711
substituting equations (29), (30), (31), (32), and (33) into equation (28) yields:
Figure FDA00025868167100000712
substituting equations (29), (30), (31), (32), and (34) into equation (28) yields:
Figure FDA00025868167100000713
during the operation of the motor, the rotor flux linkage is a bounded numerical value, thus defining
Figure FDA00025868167100000714
Where N is a normal number, the substitution of equations (22), (27), (35) and (36) into equation (25) yields:
Figure FDA00025868167100000715
Figure FDA0002586816710000081
wherein the content of the first and second substances,
Figure FDA0002586816710000082
selection of appropriate M and ΔtTo make inequality satisfy
Figure FDA0002586816710000083
Figure FDA0002586816710000084
The selected parameter satisfies
Figure FDA0002586816710000085
Error of the measurement
Figure FDA0002586816710000086
And
Figure FDA0002586816710000087
if true, obtaining that delta V (k) is less than or equal to 0;
further, it is known that for
Figure FDA0002586816710000088
If true;
the true control law v is obtained from the above analysisq(k) And vd(k) Under the action of (2), the tracking error e of the discrete system of the asynchronous motor1(k) And e4(k) It is possible to converge to a sufficiently small neighborhood of the origin and ensure that the other signals are bounded.
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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5576632A (en) * 1994-06-30 1996-11-19 Siemens Corporate Research, Inc. Neural network auto-associator and method for induction motor monitoring
KR20110059690A (en) * 2011-04-18 2011-06-03 부산대학교 산학협력단 Method for stator shorted turn fault detection using opposed pair-phase voltage/current of electric motor
CN106712615A (en) * 2016-12-23 2017-05-24 重庆大学 Control method of gas turbine generator set in hybrid energy power system
CN107065540A (en) * 2017-03-15 2017-08-18 东北电力大学 A kind of adaptive dynamic surface distribution control method based on neutral net
CN108964545A (en) * 2018-07-30 2018-12-07 青岛大学 A kind of synchronous motor neural network contragradience Discrete Control Method based on command filtering
CN110112971A (en) * 2019-05-15 2019-08-09 青岛大学 A kind of asynchronous motor Position Tracking Control method based on finite time dynamic surface

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5576632A (en) * 1994-06-30 1996-11-19 Siemens Corporate Research, Inc. Neural network auto-associator and method for induction motor monitoring
KR20110059690A (en) * 2011-04-18 2011-06-03 부산대학교 산학협력단 Method for stator shorted turn fault detection using opposed pair-phase voltage/current of electric motor
CN106712615A (en) * 2016-12-23 2017-05-24 重庆大学 Control method of gas turbine generator set in hybrid energy power system
CN107065540A (en) * 2017-03-15 2017-08-18 东北电力大学 A kind of adaptive dynamic surface distribution control method based on neutral net
CN108964545A (en) * 2018-07-30 2018-12-07 青岛大学 A kind of synchronous motor neural network contragradience Discrete Control Method based on command filtering
CN110112971A (en) * 2019-05-15 2019-08-09 青岛大学 A kind of asynchronous motor Position Tracking Control method based on finite time dynamic surface

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
基于神经网络的异步电动机随机自适应动态面控制;刘丽超;《中国优秀硕士学位论文全文数据库（信息科技辑）》;20180215(第2期);全文 *

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