CN110705034B - Event trigger-based permanent magnet synchronous motor position tracking control method - Google Patents

Event trigger-based permanent magnet synchronous motor position tracking control method Download PDF

Info

Publication number
CN110705034B
CN110705034B CN201910848967.8A CN201910848967A CN110705034B CN 110705034 B CN110705034 B CN 110705034B CN 201910848967 A CN201910848967 A CN 201910848967A CN 110705034 B CN110705034 B CN 110705034B
Authority
CN
China
Prior art keywords
permanent magnet
synchronous motor
magnet synchronous
axis
disturbance
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.)
Active
Application number
CN201910848967.8A
Other languages
Chinese (zh)
Other versions
CN110705034A (en
Inventor
王敏
黄锐鹏
戴诗陆
杨辰光
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.)
Foshan Newhinken Intelligent Technology Co ltd
South China University of Technology SCUT
Original Assignee
Foshan Newhinken Intelligent Technology Co ltd
South China University of Technology SCUT
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 Foshan Newhinken Intelligent Technology Co ltd, South China University of Technology SCUT filed Critical Foshan Newhinken Intelligent Technology Co ltd
Priority to CN201910848967.8A priority Critical patent/CN110705034B/en
Publication of CN110705034A publication Critical patent/CN110705034A/en
Application granted granted Critical
Publication of CN110705034B publication Critical patent/CN110705034B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • 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
    • H02P21/13—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
    • 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
    • H02P23/0018—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
    • H02P23/00—Arrangements or methods for the control of AC motors characterised by a control method other than vector control
    • H02P23/12—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
    • 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
    • H02P25/022—Synchronous motors
    • 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
    • H02P6/00—Arrangements for controlling synchronous motors or other dynamo-electric motors using electronic commutation dependent on the rotor position; Electronic commutators therefor
    • H02P6/34—Modelling or simulation for control purposes
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L47/00—Traffic control in data switching networks
    • H04L47/10—Flow control; Congestion 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
    • H02P2207/00—Indexing scheme relating to controlling arrangements characterised by the type of motor
    • H02P2207/05—Synchronous machines, e.g. with permanent magnets or DC excitation

Landscapes

  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Biophysics (AREA)
  • General Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Biomedical Technology (AREA)
  • Signal Processing (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Control Of Ac Motors In General (AREA)

Abstract

The invention discloses a permanent magnet synchronous motor position tracking control method based on event triggering, which comprises the following steps: establishing a dynamic model and an expected tracking track of the permanent magnet synchronous motor; discretizing a permanent magnet synchronous motor system; designing an event trigger mechanism acting on a network channel between a controller and an actuator; designing an adaptive neural network controller based on event triggering; designing a disturbance observer; a feedforward compensator is designed to compensate for both matched and unmatched disturbances. The method designed by the invention not only can obtain good position tracking performance and anti-interference capability, but also can popularize the traditional time trigger control mode of the permanent magnet synchronous motor into an event trigger control mode, thereby effectively saving the resources of network bandwidth, calculation, energy and the like of a networked control system of the permanent magnet synchronous motor and ensuring that the system can normally run under the condition of network congestion.

Description

Event trigger-based permanent magnet synchronous motor position tracking control method
Technical Field
The invention relates to the technical field of permanent magnet synchronous motor position tracking control, in particular to a permanent magnet synchronous motor position tracking control method based on event triggering.
Background
The permanent magnet synchronous motor is a motor which is excited by permanent magnets and rotates synchronously with a stator space magnetic field. Compared with the traditional electric excitation motor and induction motor, the permanent magnet synchronous motor has the obvious advantages of simple structure, small volume, high efficiency, high power density and the like, and has been applied to the fields of position control, driving traction, high-efficiency power output and the like. In addition, a network is introduced into the control system to design a networked control system, so that the traditional point-to-point control mode is changed, and the method has wide development prospect and research significance. The permanent magnet synchronous motor is used as an important execution mechanism in a control system, networking becomes an important development trend, and under the continuously expanded application requirements and the large background of intelligent manufacturing industry, the permanent magnet synchronous motor control system is developing towards the directions of high performance, digitalization, intellectualization and networking due to the excellent performance of the permanent magnet synchronous motor control system.
Due to the characteristics of multivariable, nonlinearity, strong coupling and the like of the permanent magnet synchronous motor, the traditional control theory cannot meet the high performance requirement. Therefore, the application of various intelligent control methods to the permanent magnet synchronous motor control system has important application value. In addition, in practical applications, the permanent magnet synchronous motor system is usually affected by external disturbances, resulting in poor control performance. Therefore, how to improve the anti-interference capability of the system also has important application significance; in addition, the application of industrial wireless networks and various field buses is the development trend of factory automation engineering technology, a permanent magnet synchronous motor is used as an important execution mechanism in a control system, networking is an important development trend, however, most of the control systems at present adopt a control mode based on a time trigger mechanism, a sensor in the control system samples the state of an object according to a fixed period and sends the state to a controller, the controller also updates a control law according to the fixed period and sends the state to an actuator, and a redundant sampling signal wastes resources such as limited network bandwidth, calculation, energy and the like.
Disclosure of Invention
The invention aims to provide a permanent magnet synchronous motor position tracking control method based on event triggering aiming at the defects of the prior art. Aiming at the problem that a permanent magnet synchronous motor cannot be accurately modeled, the method utilizes the universal approximation theorem of the neural network and combines a reverse-reasoning design method to realize self-adaptive neural network control; aiming at the problem of limited network bandwidth resources, the method introduces an event trigger mechanism into a network channel between a controller and an actuator, reduces unnecessary signal transmission and achieves the aim of effectively saving the network bandwidth resources; aiming at the problems of matching and non-matching disturbance of a permanent magnet synchronous motor control system, the method adopts a disturbance observer to estimate the disturbance and combines a reverse control method to design a feedforward compensator, thereby improving the anti-jamming capability of the system.
The purpose of the invention can be realized by the following technical scheme:
the invention provides a permanent magnet synchronous motor position tracking control method based on event triggering, which comprises the following steps:
s1: constructing a dynamic model and an expected tracking track of the permanent magnet synchronous motor: constructing a q-d axis model of the permanent magnet synchronous motor by taking the position of a rotor of the permanent magnet synchronous motor, the angular speed of the rotor, the q-axis current and the d-axis current as state variables, and constructing an expected tracking track of the position of the rotor and the d-axis current;
s2: discretizing a dynamic model of the permanent magnet synchronous motor by adopting a first-order backward difference method;
s3: constructing an event trigger mechanism acting on a network channel between a controller and an actuator:
defining the signal transmission error of a network channel between the controller and the actuator as follows:
ue(k)＝ut(k)-u(k)
wherein u (k) ═ u1(k),u2(k)]T，ut(k)＝[ut1(k),ut2(k)]TAnd ue(k)＝[ue1(k),ue2(k)]T；u1(k),u2(k) Output signals u of controllers on the q axis and the d axis of the permanent magnet synchronous motor system respectivelyt1(k),ut2(k) Respectively being permanent magnet synchronous motor systemsLet the q-axis and d-axis actuator input signals ue1(k),ue2(k) Transmission errors of signals at two ends of a network channel on a q axis and a d axis of a permanent magnet synchronous motor system are respectively transmitted;
the triggering conditions for designing the network channel transmission signals are as follows:
|uei(k)|≥mi
wherein m isiMore than or equal to 0 is the design parameter in the trigger condition, when miWhen the value is 0, the event trigger mechanism is degraded to a time trigger mechanism; event triggered time series k τi1,2 is represented as:
Figure GDA0002965831500000021
wherein, tau is a natural number, kτiFor the most recent event trigger time, kτi+1Triggering the moment for the next event; in [ k ]τi,kτi+1) In the time interval, when the trigger condition is satisfied, uti(k) Is updated to ui(k) At the triggering time kτi+1Value u ofi(kτi+1) Otherwise uti(k) Is always kept as u under the action of a zero-order retaineri(k) At the triggering time kτiValue u ofi(kτi)；
S4: designing an adaptive neural network controller based on event triggering: introducing an event trigger mechanism into a network channel between a controller and an actuator, and constructing an adaptive neural network controller based on event trigger by combining a reverse-thrust design method;
the adaptive neural network controller is represented as:
Figure GDA0002965831500000031
Figure GDA0002965831500000032
Figure GDA0002965831500000033
wherein, g2，g3And g4As a result of the parameters of the system,
Figure GDA0002965831500000034
a state variable representing the state of the system,
Figure GDA0002965831500000035
and
Figure GDA0002965831500000036
which is a non-linear function known to the system,
Figure GDA0002965831500000037
as an estimate of the weight of the neural network, k11-BT/J, J and B denote system parameters, T denotes the sampling period, ζi,γiAnd σiRepresents a design constant, ei(i ═ 2,3,4) represents a tracking error variable, Z1(k),Z2(k) And Z3(k) Representing the input signal of a neural network, S (Z) ═ S1(Z),S2(Z),...,Sl(Z)]TRepresenting the vector of the basis function of the neural network, the positive integer l is the number of nodes of the neural network,
Figure GDA0002965831500000038
{I1,I2,...,Ilis a set of l unordered subsets with respect to {1, 2., m },
Figure GDA0002965831500000039
is a hyperbolic tangent function;
s5: constructing a disturbance observer according to a system dynamic model;
s6: constructing a feedforward compensator for compensating the matched and unmatched disturbance: aiming at the matched disturbance, compensating by adopting a disturbance estimation value of a disturbance observer; for non-matching disturbance, the non-matching disturbance located in the non-matching channel is transferred to the matching channel for compensation through a reverse-push design method;
the feedforward compensator on the q axis and the d axis of the permanent magnet synchronous motor system is constructed as follows:
Figure GDA00029658315000000310
Figure GDA00029658315000000311
wherein the content of the first and second substances,
Figure GDA00029658315000000312
represents the output signal of the disturbance observer,
Figure GDA00029658315000000313
and
Figure GDA00029658315000000314
a compensation function designed in the reverse-deducing design process;
s7: the input signal of the permanent magnet synchronous motor networked control system is designed as follows:
Figure GDA00029658315000000315
wherein i is 1, 2.
As a preferred technical solution, the dynamic model of the permanent magnet synchronous motor in step S1 is:
Figure GDA0002965831500000041
Figure GDA0002965831500000042
Figure GDA0002965831500000043
Figure GDA0002965831500000044
y1＝θ
y2＝id
wherein, theta, omega, iqAnd idThe system state is respectively the rotor position, the rotor angular velocity, the q-axis current and the d-axis current of the motor; u. ofqAnd udIs a system input, q-axis voltage and d-axis voltage respectively; y is1And y2Is the system output, which is the rotor position and d-axis current, respectively; j, np,B,L,Rs,TLAnd Φ are system parameters, rotational inertia, pole pair, viscous friction, stator inductance, stator resistance, load torque and magnetic flux, respectively; Δ fω(ω),Δfq(ω，iq,id) And Δ fd(ω，iq,id) Is a system unknown dynamic; dωIs a non-matching disturbance outside the system, dqAnd ddIs a system external match perturbation.
As a preferred technical solution, the expected tracking trajectory of the permanent magnet synchronous motor in step S1 is:
yd1＝fd(θ)
yd2＝0
wherein, yd1Is y1Desired track of fd(θ) is a known function, yd2Is y2Is designed to be zero for maintaining constant current operation.
As a preferred technical solution, in step S2, the dynamic model of the permanent magnet synchronous motor is discretized by using a first-order backward difference method, and a specific calculation formula is as follows:
θ(k+1)＝θ(k)+Tω(k)
Figure GDA0002965831500000045
Figure GDA0002965831500000046
Figure GDA0002965831500000047
wherein, T represents the sampling period, and the calculation formula is simplified as follows:
x1(k+1)＝x1(k)+g1x2(k)
x2(k+1)＝f2(x2(k))+Δf2(x2(k))+g2x3(k)+d1(k)
Figure GDA0002965831500000051
Figure GDA0002965831500000052
wherein x is1(k)＝θ(k),x2(k)＝ω(k),x3(k)＝iq(k),x4(k)＝id(k),
Figure GDA0002965831500000053
f2(x2(k))＝(1-BT/J)ω(k)-TLT/J,
Figure GDA0002965831500000054
Figure GDA0002965831500000055
g1＝T,g2＝3npΦT/(2J),g3＝T/L,g4＝T/L,Δf2(x2(k))＝TΔfω(ω(k)),
Figure GDA0002965831500000056
Figure GDA0002965831500000057
d1(k)＝Tdω(k),d2(k)＝Tdq(k),d3(k)＝Tdd(k)。
Preferably, the design process error variable e of the controller in step S4iWherein i ∈ [1,2,3,4 ]]Said error variable eiThe concrete expression is as follows:
e1(k)＝x1(k)-yd1(k)
e2(k)＝x2(k)-α1(k)
e3(k)＝x3(k)-α2(k)
e4(k)＝x4(k)
Figure GDA0002965831500000058
Figure GDA0002965831500000059
wherein alpha is1(k) And alpha2(k) Representing a virtual control quantity.
As a preferred technical solution, in step S5, constructing a disturbance observer according to the system dynamics model is specifically represented as:
Figure GDA00029658315000000510
Figure GDA00029658315000000511
Figure GDA00029658315000000512
Figure GDA00029658315000000513
wherein z isd(k)＝[zd1(k),zd2(k),zd3(k)]TTo perturb the internal state of the observer, Λ ═ diag { λ ═ λ1，λ2,λ3Denotes the design parameters of the disturbance observer,
Figure GDA00029658315000000514
denotes the disturbance estimate, x (k) ═ x2(k),x3(k),x4(k)]T，
Figure GDA00029658315000000515
Denotes a known nonlinear term, g ═ g2,g3,g4]TRepresenting a known constant.
As a preferred solution, in step S6, the compensation function of the feedforward compensator is designed as follows:
Figure GDA0002965831500000061
Figure GDA0002965831500000062
wherein the content of the first and second substances,
Figure GDA0002965831500000063
representing the output signal of the disturbance observer.
Compared with the prior art, the invention has the following advantages and beneficial effects:
1. the invention realizes the self-adaptive neural network control by designing the self-adaptive neural network controller, utilizing the universal approximation theorem of the neural network and combining a backstepping control method, and solves the problem of control performance caused by the fact that the permanent magnet synchronous motor cannot be accurately modeled.
2. The invention estimates the disturbance by designing the disturbance observer, and designs the feedforward compensator by combining the backstepping control method, thereby solving the problems of matching and non-matching disturbance of the permanent magnet synchronous motor control system and improving the anti-jamming capability of the system.
3. The invention solves the problem of cause-effect contradiction caused by non-matching disturbance in the design process of the controller by designing a reasonable disturbance observer and combining a reverse control method.
4. The invention introduces the event trigger mechanism by designing the event trigger mechanism aiming at the problem of limited network bandwidth resources, reduces unnecessary signal transmission and achieves the aim of effectively saving the network bandwidth resources.
Drawings
Fig. 1 is a schematic diagram of a permanent magnet synchronous motor system.
Fig. 2 is an overall control block diagram of the permanent magnet synchronous motor according to the embodiment.
Fig. 3 is a tracking trace diagram of the position and the expected position of the present embodiment.
Fig. 4 is a graph showing the variation of the position tracking error in the present embodiment.
Fig. 5 is a graph comparing the number of triggers required for the q-axis event trigger control and the time trigger control according to the present embodiment.
FIG. 6 is a diagram showing the trigger time interval of the q-axis in the present embodiment.
Fig. 7 is a graph showing the variation of the system input on the q-axis in the present embodiment.
Fig. 8 is a d-axis current variation graph of the present embodiment.
Fig. 9 is a graph comparing the number of triggers required for the event trigger control and the time trigger control of the d-axis of the present embodiment.
FIG. 10 is a d-axis representation of event trigger intervals according to this embodiment.
Fig. 11 is a system input variation graph of the d-axis of the present embodiment.
FIG. 12 is a diagram illustrating a simulation of weight convergence of a neural network according to the present embodiment.
Fig. 13 is a diagram of unknown dynamic effect of the neural network approximation system according to the embodiment.
FIG. 14 is a graph illustrating the variation of disturbance and disturbance observed quantity in the present embodiment.
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.
Example (b):
as shown in fig. 1 and fig. 2, the embodiment provides a method for controlling position tracking of a permanent magnet synchronous motor based on event triggering, and the detailed implementation process includes:
step 1, establishing a dynamic model of the permanent magnet synchronous motor:
Figure GDA0002965831500000071
Figure GDA0002965831500000072
Figure GDA0002965831500000073
Figure GDA0002965831500000074
y1＝θ
y2＝id
wherein, theta, omega, iqAnd idThe system state is respectively the rotor position, the rotor angular velocity, the q-axis current and the d-axis current of the motor; u. ofqAnd udIs a system input, q-axis voltage and d-axis voltage respectively; y is1And y2Is the system output, which is the rotor position and d-axis current, respectively; j, np,B,L,Rs,TLAnd phi is a system parameter, which is respectively rotational inertia, pole pair, viscous friction, stator inductance, and stator electricityResistance, load torque and magnetic flux; Δ fω(ω),Δfq(ω，iq,id) And Δ fd(ω，iq,id) Is a system unknown dynamic; dωIs a non-matching disturbance outside the system, dqAnd ddIs a system external match perturbation.
Establishing an expected tracking track of the permanent magnet synchronous motor:
yd1＝fd(θ)
yd2＝0
wherein, y1dIs y1Desired track of fd(θ) is a known function, y2dIs y2Is expected to track and is designed to be zero to maintain constant current operation.
The relevant parameters of the permanent magnet synchronous motor model selected in the embodiment are respectively as follows:
J＝0.00375Kgm2,np＝3,B＝0.001158Nm/(rad/s),L＝0.00315H,Rs＝0.68Ω,TL＝1Nm,Φ＝0.1245H,dω＝2sin(πk),dq＝0.8sin(πk),dd＝0.3sin(πk),Δfω(ω)＝0.1ω-0.15,Δfq(ω，iq,id)＝-0.02iq-0.07ω-0.002ωid,Δfd(ω，iq,id)＝-0.02id+0.002ωiq。
the expected tracking trajectory given in this embodiment is
Figure GDA0002965831500000081
y2d＝0。
Step 2, discretizing a dynamic model of the permanent magnet synchronous motor into:
θ(k+1)＝θ(k)+Tω(k)
Figure GDA0002965831500000082
Figure GDA0002965831500000083
Figure GDA0002965831500000084
where T is the sampling period. For ease of presentation, the above equation is abbreviated as:
x1(k+1)＝x1(k)+g1x2(k)
x2(k+1)＝f2(x2(k))+Δf2(x2(k))+g2x3(k)+d1(k)
Figure GDA0002965831500000085
Figure GDA0002965831500000086
wherein x is1(k)＝θ(k),x2(k)＝ω(k),x3(k)＝iq(k),x4(k)＝id(k),
Figure GDA0002965831500000087
f2(x2(k))＝(1-BT/J)ω(k)-TLT/J,
Figure GDA0002965831500000088
Figure GDA0002965831500000089
g1＝T,g2＝3npΦT/(2J),g3＝T/L,g4＝T/L,Δf2(x2(k))＝TΔfω(ω(k)),
Figure GDA00029658315000000810
Figure GDA00029658315000000811
d1(k)＝Tdω(k),d2(k)＝Tdq(k),d3(k)＝Tdd(k)。
The sampling period parameter in this embodiment is designed to be T ═ 0.0055, and the system initial state is set to be x1(k)＝0.3,x2(k)＝0.5,x3(k)＝0.5,x4(k)＝0.5。
Step 3, designing an event trigger mechanism acting on a network channel between the controller and the actuator:
defining the signal transmission error of a network channel between the controller and the actuator as follows:
ue(k)＝ut(k)-u(k)
wherein u (k) ═ u1(k),u2(k)]T，ut(k)＝[ut1(k),ut2(k)]TAnd ue(k)＝[ue1(k),ue2(k)]T；u1(k),u2(k) Output signals u of controllers on the q axis and the d axis of the permanent magnet synchronous motor system respectivelyt1(k),ut2(k) The input signals u of the actuators on the q axis and the d axis of the permanent magnet synchronous motor system are respectivelye1(k),ue2(k) Transmission errors of signals at two ends of a network channel on a q axis and a d axis of a permanent magnet synchronous motor system are respectively transmitted;
the triggering conditions for designing the network channel transmission signals are as follows:
|uei(k)|≥mi,(i＝1,2)
wherein m isiMore than or equal to 0 is the design parameter in the trigger condition, when miWhen 0, the event-triggered mechanism degenerates to a time-triggered mechanism.
Thus, the time series of event triggers k τi1,2 can be represented as:
Figure GDA0002965831500000091
wherein k isτiFor the most recent event trigger time, kτi+1The next event trigger time. In [ k ]τi,kτi+1) In the time interval, when the trigger condition is satisfied, uti(k) Will be updated to ui(k) At the triggering time kτi+1Value u ofi(kτi+1) Otherwise uti(k) Will be kept as u all the time by the action of the zero-order keeperi(k) At the last trigger time kτiValue u ofi(kτi)。
The parameters of the event trigger mechanism in this embodiment are designed as follows: m is1＝0.1,m2＝0.05。
Step 4, designing the adaptive neural network controller based on event triggering as follows:
a reverse-deducing design method is adopted, and a system error equation is defined as follows:
e1(k)＝x1(k)-yd(k)
e2(k)＝x2(k)-α1(k)
e3(k)＝x3(k)-α2(k)
e4(k)＝x4(k)
wherein alpha is1(k) And alpha2(k) Is a virtual control quantity.
According to the stability analysis of the Lyapunov function, the virtual control quantity is designed as follows:
Figure GDA0002965831500000092
Figure GDA0002965831500000093
designing the self-adaptive neural network controller as follows:
Figure GDA0002965831500000101
Figure GDA0002965831500000102
Figure GDA0002965831500000103
wherein the content of the first and second substances,
Figure GDA0002965831500000104
is an estimate of the weight of the neural network, ζi,γi,σiTo design a constant, Z1(k)＝x2(k),Z2(k)＝[x2(k),x3(k),x4(k)]TAnd Z3(k)＝[x2(k),x3(k),x4(k)]TFor the input signal of the neural network, the basis function vector is selected as S (Z) ═ S1(Z),S2(Z),...,Sl(Z)]TThe positive integer l is the number of neural network nodes,
Figure GDA0002965831500000105
{I1,I2,...,Ilis a set of l unordered subsets with respect to {1, 2., m },
Figure GDA0002965831500000106
is a hyperbolic tangent function.
Introducing the event trigger mechanism in the step 4 into a network channel between the adaptive neural network controller and the actuator to obtain the adaptive neural network controller u based on event triggerti(k)。
The number of nodes of Shen 3 neural networks in the embodiment is respectively designed as follows: l1＝5,l2＝19,l 210. The weight update rate parameters of the neural network are respectively designed as zeta1＝1,γ1＝0.08,σ1＝0.1；ζ2＝0.9,γ2＝0.09,σ20.05 and ζ3＝1,γ3＝0.01,σ3＝0.1。
Step 5, designing a disturbance observer as follows:
Figure GDA0002965831500000107
Figure GDA0002965831500000108
wherein z isd(k)＝[zd1(k),zd2(k),zd3(k)]TTo perturb the internal state of the observer, Λ ═ diag { λ ═ λ1，λ2,λ3The design parameters of the disturbance observer are adopted,
Figure GDA0002965831500000109
for the disturbance estimation, x (k) ═ x2(k),x3(k),x4(k)]T，
Figure GDA00029658315000001010
g＝[g2,g3,g4]T，
Figure GDA00029658315000001011
Figure GDA00029658315000001012
Is the result of approximating the unknown dynamics of the system using a neural network.
The parameters of the disturbance observer in this embodiment are designed as follows: lambda [ alpha ]1＝0.5，λ2＝0.5,λ3＝0.5。
Step 6, designing a feedforward compensator for compensating the matching disturbance and the non-matching disturbance as follows:
aiming at the matched disturbance, directly using a disturbance estimation value of a disturbance observer to compensate; and aiming at the non-matching disturbance, the non-matching disturbance positioned in the non-matching channel is transferred to the matching channel for compensation by a reverse-push design method. Designing feed-forward compensators on a q axis and a d axis of a permanent magnet synchronous motor system as follows:
Figure GDA0002965831500000111
Figure GDA0002965831500000112
and 7, inputting signals of the network control system of the permanent magnet synchronous motor into the following steps:
Figure GDA0002965831500000113
in this embodiment, the combination simulation result and the curve chart are analyzed as follows:
as shown in fig. 3 and 4, it can be obtained from a tracking trajectory diagram of the position and the expected position and a change curve diagram of the position tracking error, the position tracking performance of the permanent magnet synchronous motor is good, and the position tracking error converges in a small neighborhood of zero;
as shown in fig. 5 and 6, it can be obtained from the comparison graph of the event trigger control of the q-axis and the number of trigger times required by the time trigger control, and the representation graph of the event trigger time interval of the q-axis that the time trigger control mode needs to be triggered 800 times, but the event trigger control mode only needs to be triggered 500 times, so that the event trigger control mode can effectively reduce the number of trigger times on the basis of ensuring the position tracking performance, thereby saving the network bandwidth resources of the system;
as shown in fig. 7, it can be derived from the q-axis system input variation graph that the q-axis system input is bounded, and meanwhile, the given system input is stable, which ensures that the system can be used in practice;
as shown in fig. 8, 9, and 10, by combining the graphs of the trigger times required for the event trigger control and the time trigger control of the d-axis, it can be obtained that the event trigger control mode of the d-axis is triggered only 9 times, which enables the convergence error of the d-axis current to be within the acceptable range and greatly reduces the trigger times of the event;
as shown in fig. 11, it can be obtained in the graph of the change of the system input of the d-axis that the system input of the d-axis is bounded, and meanwhile, the given system input is stable, which ensures that the system can be used in practice;
as shown in fig. 12, it can be derived from the neural network weight convergence simulation diagram that the weights of 3 neural networks are bounded;
as shown in fig. 13, it can be obtained from the effect graphs that 3 neural networks respectively approach 3 system unknown dynamics, and the approach effect of the embodiment is good;
as shown in fig. 14, it can be seen from the disturbance and disturbance observation change graph that the disturbance observer can well estimate the amount of the unmeasured disturbance, and the reason that the disturbance estimation value in the disturbance and disturbance observation change graph deviates is that the disturbance estimation value not only estimates the amount of the disturbance, but also estimates the approximation error generated when the neural network approximates the unknown dynamic state of the system.
Fig. 14 is a graph of the change of the disturbance and the disturbance observed quantity, and it can be seen that the disturbance observer can well estimate the unmeasured disturbance quantity. Further, the reason why the disturbance estimated value in fig. 14 is biased is that the disturbance estimated value estimates not only the disturbance amount but also an approximation error generated when the neural network approximates the unknown dynamics of the system.
The above description is only for the preferred embodiments of the present invention, but the protection scope of the present invention is not limited thereto, and any person skilled in the art can substitute or change the technical solution of the present invention and the inventive concept within the scope of the present invention, which is disclosed by the present invention, and the equivalent or change thereof belongs to the protection scope of the present invention.

Claims (7)

1. A permanent magnet synchronous motor position tracking control method based on event triggering is characterized by comprising the following steps:
s1: constructing a dynamic model and an expected tracking track of the permanent magnet synchronous motor: constructing a q-d axis model of the permanent magnet synchronous motor by taking the position of a rotor of the permanent magnet synchronous motor, the angular speed of the rotor, the q-axis current and the d-axis current as state variables, and constructing an expected tracking track of the position of the rotor and the d-axis current;
s2: discretizing a dynamic model of the permanent magnet synchronous motor by adopting a first-order backward difference method;
s3: constructing an event trigger mechanism acting on a network channel between a controller and an actuator;
defining the signal transmission error of a network channel between the controller and the actuator as follows:
ue(k)＝ut(k)-u(k)
wherein u (k) ═ u1(k),u2(k)]T，ut(k)＝[ut1(k),ut2(k)]TAnd ue(k)＝[ue1(k),ue2(k)]T；u1(k),u2(k) Output signals u of controllers on the q axis and the d axis of the permanent magnet synchronous motor system respectivelyt1(k),ut2(k) The input signals u of the actuators on the q axis and the d axis of the permanent magnet synchronous motor system are respectivelye1(k),ue2(k) Transmission errors of signals at two ends of a network channel on a q axis and a d axis of a permanent magnet synchronous motor system are respectively transmitted;
the triggering conditions for designing the network channel transmission signals are as follows:
|uei(k)|≥mi
wherein i is 1,2, miMore than or equal to 0 is the design parameter in the trigger condition, when miWhen the value is 0, the event trigger mechanism is degraded to a time trigger mechanism;
event triggered time series kτi1,2 is represented as:
Figure FDA0002965831490000011
where i is 1,2, τ is a natural number, kτiFor the most recent event trigger time, kτi+1Triggering the moment for the next event; in [ k ]τi,kτi+1) In the time interval, when the trigger condition is satisfied, uti(k) Is updated to ui(k) At the triggering time kτi+1Value u ofi(kτi+1) Otherwise uti(k) Is always kept as u under the action of a zero-order retaineri(k) At the triggering time kτiValue u ofi(kτi)；
S4: designing an adaptive neural network controller based on event triggering: introducing an event trigger mechanism into a network channel between a controller and an actuator, and constructing an adaptive neural network controller based on event trigger by combining a reverse-thrust design method;
the adaptive neural network controller is represented as:
Figure FDA0002965831490000021
Figure FDA0002965831490000022
Figure FDA0002965831490000023
wherein, g2，g3And g4As a result of the parameters of the system,
Figure FDA0002965831490000024
a state variable representing the state of the system,
Figure FDA0002965831490000025
and
Figure FDA0002965831490000026
which is a non-linear function known to the system,
Figure FDA0002965831490000027
as an estimate of the weight of the neural network, k11-BT/J, J and B denote system parameters, T denotes the sampling period, ζi,γiAnd σiRepresents a design constant, ei(i ═ 2,3,4) represents a tracking error variable, Z1(k),Z2(k) And Z3(k) Representing the input signal of a neural network, S (Z) ═ S1(Z),S2(Z),...,Sl(Z)]TRepresenting the vector of the basis function of the neural network, the positive integer l is the number of nodes of the neural network,
Figure FDA0002965831490000028
{I1,I2,...,Ilis a set of l unordered subsets with respect to {1, 2., m },
Figure FDA0002965831490000029
is a hyperbolic tangent function;
s5: constructing a disturbance observer according to a system dynamic model;
s6: constructing a feedforward compensator for compensating the matched and unmatched disturbance: aiming at the matched disturbance, compensating by adopting a disturbance estimation value of a disturbance observer; for non-matching disturbance, the non-matching disturbance located in the non-matching channel is transferred to the matching channel for compensation through a reverse-push design method;
the feedforward compensator on the q axis and the d axis of the permanent magnet synchronous motor system is constructed as follows:
Figure FDA00029658314900000210
Figure FDA00029658314900000211
wherein the content of the first and second substances,
Figure FDA00029658314900000212
represents the output signal of the disturbance observer,
Figure FDA00029658314900000213
and
Figure FDA00029658314900000214
a compensation function designed in the reverse-deducing design process;
s7: the input signal of the permanent magnet synchronous motor networked control system is designed as follows:
Figure FDA00029658314900000215
wherein i is 1, 2.
2. The method according to claim 1, wherein the dynamic model of the PMSM in step S1 is:
Figure FDA0002965831490000031
Figure FDA0002965831490000032
Figure FDA0002965831490000033
Figure FDA0002965831490000034
y1＝θ
y2＝id
wherein, theta, omega, iqAnd idThe system state is respectively the rotor position, the rotor angular velocity, the q-axis current and the d-axis current of the motor; u. ofqAnd udIs a system input, q-axis voltage and d-axis voltage respectively; y is1And y2Is the system output, which is the rotor position and d-axis current, respectively; j, np,B,L,Rs,TLAnd Φ are system parameters, rotational inertia, pole pair, viscous friction, stator inductance, stator resistance, load torque and magnetic flux, respectively;Δfω(ω),Δfq(ω，iq,id) And Δ fd(ω，iq,id) Is a system unknown dynamic; dωIs a non-matching disturbance outside the system, dqAnd ddIs a system external match perturbation.
3. The method for controlling position tracking of the permanent magnet synchronous motor based on event triggering according to claim 1 or 2, wherein the expected tracking trajectory of the permanent magnet synchronous motor in step S1 is as follows:
yd1＝fd(θ)
yd2＝0
wherein, yd1Is y1Desired track of fd(θ) is a known function, yd2Is y2Is designed to be zero for maintaining constant current operation.
4. The method according to claim 1, wherein in step S2, the dynamic model of the permanent magnet synchronous motor is discretized by a first-order backward difference method, and a specific calculation formula is:
θ(k+1)＝θ(k)+Tω(k)
Figure FDA0002965831490000035
Figure FDA0002965831490000036
Figure FDA0002965831490000037
where T represents the sampling period, θ, ω, iqAnd idIs a system state, which is the rotor position, rotor angular velocity and q-axis current of the motorAnd d-axis current; j, np,B,L,Rs,TLAnd Φ are system parameters, rotational inertia, pole pair, viscous friction, stator inductance, stator resistance, load torque and magnetic flux, respectively; u. ofqAnd udIs a system input, q-axis voltage and d-axis voltage respectively; Δ fω(ω),Δfq(ω，iq,id) And Δ fd(ω，iq,id) Is a system unknown dynamic; dωIs a non-matching disturbance outside the system, dqAnd ddIs a system external match disturbance; the calculation formula is simplified as follows:
x1(k+1)＝x1(k)+g1x2(k)
x2(k+1)＝f2(x2(k))+Δf2(x2(k))+g2x3(k)+d1(k)
Figure FDA0002965831490000041
Figure FDA0002965831490000042
wherein x is1(k)＝θ(k),x2(k)＝ω(k),x3(k)＝iq(k),x4(k)＝id(k),
Figure FDA0002965831490000043
f2(x2(k))＝(1-BT/J)ω(k)-TLT/J,
Figure FDA0002965831490000044
Figure FDA0002965831490000045
g1＝T,g2＝3npΦT/(2J),g3＝T/L,g4＝T/L,Δf2(x2(k))＝TΔfω(ω(k)),
Figure FDA0002965831490000046
Figure FDA0002965831490000047
d1(k)＝Tdω(k),d2(k)＝Tdq(k),d3(k)＝Tdd(k)。
5. The method as claimed in claim 1, wherein the controller design process error variable e in step S4iWherein i ∈ [1,2,3,4 ]]Said error variable eiThe concrete expression is as follows:
e1(k)＝x1(k)-yd1(k)
e2(k)＝x2(k)-α1(k)
e3(k)＝x3(k)-α2(k)
e4(k)＝x4(k)
Figure FDA0002965831490000048
Figure FDA0002965831490000049
wherein alpha is1(k) And alpha2(k) Representing a virtual control quantity.
6. The method according to claim 1, wherein the step S5 of constructing the disturbance observer according to the system dynamics model is specifically represented as:
Figure FDA0002965831490000051
Figure FDA0002965831490000052
Figure FDA0002965831490000053
Figure FDA0002965831490000054
wherein z isd(k)＝[zd1(k),zd2(k),zd3(k)]TTo perturb the internal state of the observer, Λ ═ diag { λ ═ λ1，λ2,λ3Denotes the design parameters of the disturbance observer,
Figure FDA0002965831490000055
denotes the disturbance estimate, x (k) ═ x2(k),x3(k),x4(k)]T，
Figure FDA0002965831490000056
Denotes a known nonlinear term, g ═ g2,g3,g4]TRepresenting a known constant.
7. The method for controlling position tracking of permanent magnet synchronous motor based on event triggering according to claim 1, wherein in step S6, the compensation function of the feedforward compensator is designed as follows:
Figure FDA0002965831490000057
Figure FDA0002965831490000058
wherein the content of the first and second substances,
Figure FDA0002965831490000059
representing the output signal of the disturbance observer.
CN201910848967.8A 2019-09-09 2019-09-09 Event trigger-based permanent magnet synchronous motor position tracking control method Active CN110705034B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201910848967.8A CN110705034B (en) 2019-09-09 2019-09-09 Event trigger-based permanent magnet synchronous motor position tracking control method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201910848967.8A CN110705034B (en) 2019-09-09 2019-09-09 Event trigger-based permanent magnet synchronous motor position tracking control method

Publications (2)

Publication Number Publication Date
CN110705034A CN110705034A (en) 2020-01-17
CN110705034B true CN110705034B (en) 2021-07-23

Family

ID=69194884

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201910848967.8A Active CN110705034B (en) 2019-09-09 2019-09-09 Event trigger-based permanent magnet synchronous motor position tracking control method

Country Status (1)

Country Link
CN (1) CN110705034B (en)

Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111273671B (en) * 2020-03-03 2022-05-31 大连海事大学 Non-periodic communication remote observer of intelligent ship
CN111796534A (en) * 2020-07-15 2020-10-20 哈尔滨理工大学 Network random control system based on event trigger mechanism
CN112019107B (en) * 2020-08-07 2022-12-06 华东理工大学 Permanent magnet synchronous motor terminal sliding mode control method based on periodic event triggering
CN112051734B (en) * 2020-08-18 2021-07-23 华南理工大学 Wheeled mobile robot event triggering tracking control method based on deterministic learning
CN113078644B (en) * 2021-05-13 2023-02-24 贵州大学 Self-adaptive event trigger control method of permanent magnet synchronous generator coupled chaotic network system
CN117215175B (en) * 2023-09-12 2024-03-22 大连海事大学 Aeroengine event triggering comprehensive anti-interference method based on switching model

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2015126598A (en) * 2013-12-26 2015-07-06 パナソニックＩｐマネジメント株式会社 Motor control device and motor control method
CN105811831A (en) * 2014-12-29 2016-07-27 上海大郡动力控制技术有限公司 Tracking method of rotor position of salient pole permanent magnet synchronous motor in motion state
CN109873582A (en) * 2019-01-29 2019-06-11 青岛大学 Permanent magnet synchronous motor finite time Position Tracking Control method based on dynamic surface

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108649859A (en) * 2018-04-27 2018-10-12 上海交通大学 Mining traction anti-explosion permanent magnet Synchromous machine drive system and its rotor position detecting method

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2015126598A (en) * 2013-12-26 2015-07-06 パナソニックＩｐマネジメント株式会社 Motor control device and motor control method
CN105811831A (en) * 2014-12-29 2016-07-27 上海大郡动力控制技术有限公司 Tracking method of rotor position of salient pole permanent magnet synchronous motor in motion state
CN109873582A (en) * 2019-01-29 2019-06-11 青岛大学 Permanent magnet synchronous motor finite time Position Tracking Control method based on dynamic surface

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
A Novel Adaptive Neuro-Control Approach for Permanent Magnet Synchronous Motor Speed Control;Qi Wang, et al.;《Energies》;20180906;1-21 *

Also Published As

Publication number Publication date
CN110705034A (en) 2020-01-17

Similar Documents

Publication Publication Date Title
CN110705034B (en) Event trigger-based permanent magnet synchronous motor position tracking control method
CN110347044B (en) PMSM chaotic system neural network dynamic surface control method considering output constraint
CN110716506B (en) Servo system position tracking control method based on mixed sliding mode control
Wang et al. Event-based disturbance compensation control for discrete-time SPMSM with mismatched disturbances
CN112769364B (en) Fast self-adaptive anti-interference control method of direct current motor servo system
CN115890668A (en) Distributed optimization learning control method and system for robot joint module
Yang et al. Event-triggered adaptive neural control for multiagent systems with deferred state constraints
Ma et al. Filter-and Observer-Based Finite-Time Adaptive Fuzzy Control for Induction Motors Systems Considering Stochastic Disturbance and Load Variation
Zhang et al. Adaptive event-triggered fuzzy tracking control of uncertain stochastic nonlinear systems with unmeasurable states
Chen et al. Event-triggered adaptive robust control for a class of uncertain nonlinear systems with application to mechatronic system
Gao et al. Event-triggered based model-free adaptive sliding mode constrained control for nonlinear discrete-time systems
Wang et al. Event-Triggered Adaptive Output Constraint Tracking Control of Uncertain MIMO Nonlinear Systems With Sensor and Actuator Faults
Lu et al. Command filtering-based neural network control for fractional-order PMSM with input saturation
Yang et al. Predictor-based neural dynamic surface control of a nontriangular system with unknown disturbances
Zhou et al. Finite-time adaptive neural network event-triggered output feedback control for PMSMs
CN115616953A (en) Remote servo motor tracking control method based on backstepping
Ge et al. HGO and neural network based integral sliding mode control for PMSMs with uncertainty
Liu et al. Active disturbance rejection adaptive control of multi-motor synchronized driving system
Yu et al. A data-driven distributed adaptive control approach for nonlinear multi-agent systems
Zhang et al. Nonsingular practical fixed-time consensus tracking for multimotor system with input saturation
CN114257130A (en) Quadratic-value-function-based prediction speed synchronous control method suitable for double-permanent-magnet motor system
Lu et al. Anti-disturbance speed control of permanent magnet synchronous motor based on fractional order sliding mode load observer
Zhao et al. Finite-time stability of networked predictive control systems with uncertainties
Wang et al. Event-triggered model-free adaptive predictive control for nonlinear NCSs with data dropout and application in PMSM control system
Chen et al. Adaptive High-Order Sliding Mode Control for PMSM Drives with Load Disturbance

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
GR01 Patent grant
GR01 Patent grant