CN109639192A - A kind of durface mounted permanent magnet synchronous motor time-sharing control system and Time-sharing control method - Google Patents

A kind of durface mounted permanent magnet synchronous motor time-sharing control system and Time-sharing control method Download PDF

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CN109639192A
CN109639192A CN201811520966.2A CN201811520966A CN109639192A CN 109639192 A CN109639192 A CN 109639192A CN 201811520966 A CN201811520966 A CN 201811520966A CN 109639192 A CN109639192 A CN 109639192A
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permanent magnet
magnet synchronous
synchronous motor
time
finite aggregate
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CN109639192B (en
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寇鹏
冯玉涛
梁得亮
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Xian Jiaotong 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
    • 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
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/14—Estimation or adaptation of machine parameters, e.g. flux, current or voltage
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/14—Estimation or adaptation of machine parameters, e.g. flux, current or voltage
    • H02P21/18—Estimation of position or speed
    • 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
    • H02P21/22—Current control, e.g. using a current control loop
    • 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
    • H02P25/024—Synchronous motors controlled by supply frequency
    • 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
    • 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
    • H02P6/10—Arrangements for controlling torque ripple, e.g. providing reduced torque ripple
    • 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

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Control Of Ac Motors In General (AREA)

Abstract

The invention discloses a kind of durface mounted permanent magnet Synchronous Machine Models PREDICTIVE CONTROL Time-sharing control method, mainly comprising the steps that enable signal is arranged in sampling instant k in (1) is low level, enables the second finite aggregate model predictive controller and carries out idIt adjusts；(2) current value at k+1 moment is predicted according to current time；(3) it chooses so that the optimized switching state that the switch state that cost function minimizes should be taken as the k+1 moment；(4) it is high level in sampling instant k+1 setting enable signal, enables the first finite aggregate model predictive controller and carry out rotating-speed tracking；(5) tachometer value at k+2 moment is predicted according to current time；(6) it chooses so that the optimized switching state that the switch state that cost function minimizes should be taken as the k+2 moment.The invention avoids the solutions of nonlinear problem, greatly reduce amount of calculation, improve the real-time of control, while realizing the high dynamic performance of motor, reduce the torque pulsation of motor.

Description

A kind of durface mounted permanent magnet synchronous motor time-sharing control system and Time-sharing control method
Technical field
The invention belongs to permanent magnet synchronous motor control technology field, in particular to a kind of durface mounted permanent magnet synchronous motor timesharing Control system and Time-sharing control method.
Background technique
Two problems must be taken into consideration in the control of durface mounted permanent magnet synchronous motor.First is that high dynamic performance.Due to the ginseng of motor Examining revolving speed may uprush or anticlimax, it is necessary to adjust motor actual speed quickly with quick track reference revolving speed, this can be regarded as One online optimal control problem.Second is that the actual motion of motor will receive constraint, such as stator current constraint and voltage limitation. In view of both of these problems, the control of durface mounted permanent magnet synchronous motor can be regarded as a controlled online optimum control and ask Topic.Model Predictive Control is a kind of method suitable for such control problem, because it has the ability of system processing constraint.Mould Type forecast Control Algorithm can be divided into two classes: continuous control collection and limited domination set.In both methods, finite aggregate model prediction Control the control more suitable for electric drive system.Because finite aggregate Model Predictive Control directly uses the switch of power inverter State (0: closing, 1: conducting) does not need complicated pulse width modulation as control signal.
In finite aggregate Model Predictive Control, the state behavior of controlled device is predicted using prediction model.Prediction model Ordinary priority selects linear state space model, because can finally be converted to Linear Model for Prediction control problem to solve.So And durface mounted permanent magnet synchronous motor is a non-linear and strongly coupled system, is difficult to obtain in d-q coordinate system linear Mathematical model.In order to solve this problem, different solutions has been proposed in already present document.These methods are substantially Linearization technique, Multi model Predictive Controllers and the nonlinear model predictive control method based on model of mind can be divided into.Line Property method substantially thinking be: using feedback linearization technology linearizing non-linear model, pass through typical Linear Model for Prediction Control is to handle.However, this method cannot effectively handle constraint, and inearized model is only in very small working range It is interior effective；Multiple model predictive control methods are described using the piecewise linearity of nonlinear model, have calculate it is simple, can transport in real time Capable advantage, but due to all employing new inearized model at every sampling moment, the loss of multidate information is not It is evitable.Therefore, in the continually changing situation of model, it is unable to ensure the feasibility of this method；It is intended to gram there are also some This nonlinear Nonlinear Model Predictive Control based on model of mind is taken, such as Neural Network model predictive control and fuzzy mould Type PREDICTIVE CONTROL.These methods pass through training prediction model and are used to predict the behavior of nonlinear system, however the model training time It is long, it is computationally intensive the problems such as cause these methods to be also difficult to be widely applied at present.
Summary of the invention
The purpose of the present invention is to provide a kind of durface mounted permanent magnet synchronous motor time-sharing control system and Time-sharing control method, To solve the above problems.
To achieve the above object, the invention adopts the following technical scheme:
A kind of durface mounted permanent magnet synchronous motor time-sharing control system, including control module, inverter and durface mounted permanent magnet are same Walk motor；Control module connects inverter, and inverter connects surface-mount type permanent magnet synchronous motor；Control module includes the first finite aggregate Model predictive controller and the second finite aggregate model predictive controller；First finite aggregate model predictive controller and the second finite aggregate Model predictive controller is connect by gating switch with inverter；First finite aggregate model predictive controller is used for durface mounted permanent magnet The rotating-speed tracking of synchronous motor, the second finite aggregate model predictive controller are used for the i of durface mounted permanent magnet synchronous motordIt adjusts.
A kind of Time-sharing control method of durface mounted permanent magnet synchronous motor time-sharing control system, based on a kind of table described above Mounted permanent magnet synchronous motor time-sharing control system, comprising the following steps:
Step 1, controlled device nonlinear mathematical model is established, controlled device is inverter electricity synchronous with durface mounted permanent magnet Machine；
It step 2, is two linear submodels by the nonlinear model decoupling in step 1；
Step 3, sampling instant k setting enable signal be low level, enable the second finite aggregate model predictive controller into The i of row durface mounted permanent magnet synchronous motordIt adjusts；
Step 4, the current value at k+1 moment is predicted according to current time；
Step 5, it chooses so that the optimized switching state that the switch state that cost function minimizes should be taken as the k+1 moment Sabc_d；
Step 6, it is high level in sampling instant k+1 setting enable signal, enables the first finite aggregate model predictive controller Carry out rotating-speed tracking；
Step 7, the tachometer value at k+2 moment is predicted according to current time；
Step 8, it chooses so that the optimized switching state that the switch state that cost function minimizes should be taken as the k+2 moment Sabc_ω。
Further, in step 1, mathematical model includes:
1) two-level inverter models
Use vector S=[Sa, Sb, Sc]TTo indicate the switch state of two-level inverter；The switch state of every half-bridge can To be expressed as follows:
X represents certain phase bridge arm, takes A, B or C；In d-q coordinate system, by using the switch state function, stator is obtained Voltage and two-level inverter switch state relationship are as follows:
Wherein,
Here VdAnd VqIt is d axis and q axis stator voltage, VdcIt is DC bus-bar voltage, θ is rotor electrical angle；Selected Switch state S will be acted as optimum control；
2) durface mounted permanent magnet synchronous motor models
When ignoring influence and external temperature caused by core saturation, lag and eddy-current loss and humidity to motor operation The influence of permanent magnet flux linkage；And think that the magnetic field that is generated by permanent magnet is always sinusoidal, under these conditions, d-q coordinate system The mathematical model of middle durface mounted permanent magnet synchronous motor is as follows:
Here id, iq, Vd, VqThe stator current and voltage in d-q coordinate system are respectively represented, L is fixed in d-q coordinate system The equivalent inductance of sub- winding, R are stator resistance, ψfIt is permanent magnet flux linkage, pnIt is number of pole-pairs, ωmIt is rotor machinery angular speed；
Torque equation and mechanical motion equation are expressed as follows:
TeIt is electromagnetic torque, J is motor rotary inertia, TLIt is load torque；
The form that (3)-(5) are write as state-space expression is as follows:
From (6), it can be seen that, state-space model includes nonlinear terms ωmidAnd ωmiq。
Further, in step 2, decoupling includes the following:
1)iq-ωmLinear submodel
In nonlinear model (6), i is selectedqAnd ωmAs state variable, and assume id=0, then the model becomes one The state-space model of linearly invariant, it may be assumed that
By (7) further discretization and by S1=[Sa1, Sb1, Sc1]TAs input variable, submodel (7) be can be written as:
Output equation are as follows:Wherein TsIt is the sampling period, k indicates sampling instant；
In this equation, output variable is ωm；Linear submodel (8) uses the linear finite aggregate model prediction of standard Control is to realize ωmIt adjusts；
2)idLinear submodel
In (6), by idIt is set as state variable, and by iqAnd ωmIt is considered as constant, then nonlinear model (6) becomes another Linear state-space submodel:
(10) further discretization, and work as switch state S2=[Sa2, Sb2, Sc2]TInput as two-level inverter When variable:
Output equation are as follows:Herein,
Output variable is i hereind；Submodel (11) is also linearly invariant model.
Further, in step 6, the first finite aggregate model predictive controller is based on iq-ωmLinear submodel (8)-(9) It is designed；K+1 at every sampling moment, the cost function design of the first finite aggregate model predictive controller are as follows:
Y herein1(k)=[y1(k+1|k),y1(k+2|k),…,y1(k+H|k)]T,Yref1(k)=[yref1(k+1|k), yref1(k+2|k),…,yref1(k+H|k)]T, (k+h | k) indicates the value at the k+h moment predicted based on k moment available information；U1 (k) control sequence when moment k, i.e. U are indicated1(k)=[u1(k+1),u1(k+2),…,u1(k+H)]T；Wβ1=diag (β1)and W1-β1=diag (1- β1) be two optimization aims weight matrix；Prediction and control time domain are all set as H；In cost function, root The predicted value y of output is calculated according to state-space model (9)1(k+h|k)；Output valve y is predicted in this way1(k+h | k) about switch state [u1(k),u1(k+1),…,u1(k+H)]TFunction expression it is as follows:
y1(k+h | k)=C1A1 hx1(k)+C1A1 h-1(B1u1(k+1)+E1z1(k))
+C1A1 h-2(B1u1(k+2|k)+E1z1(k+1|k))+C1A1 1(B1u1(k+h|k)+E1z1(k+h-1|k)) (14)
Y1(k) it is expressed as follows in the matrix form:
Wherein
In practical applications, it is contemplated that the output capacity of two-level inverter, so should add to output voltage and electric current With limitation.We apply following linear inequality constraint:
-iq,max≤iq(k+h|k)≤iq,max, h=1,2 ..., H (16)
-Vq,max≤Vq(k+h|k)≤Vq,max, h=1,2 ..., H (17)
By above-mentioned derivation, the linear representation of the first finite aggregate model predictive controller can be indicated with matrix form Are as follows:
s.t.L1U1(k)≤K1(19)
Herein,
L1And K1It is related linear inequality constraint matrix with (16) and (17).
Further, in step 3, the second finite aggregate model predictive controller is based on idLinear submodel (11)-(12) It is designed；K at every sampling moment, the cost function design of the second finite aggregate model predictive controller are as follows:
Voltage and current inequality constraints is as follows:
-id,max≤id(k+h|k)≤id,max, h=1,2 ..., H (23)
-Vd,max≤Vd(k+h|k)≤Vd,max, h=1,2 ..., H (24)
Then the second finite aggregate model predictive controller problem is described as follows in the matrix form:
s.t.L2U2(k)≤K2 (26)
Wherein,
L2And K2It is linear inequality constraint matrix relevant to (23) and (24).
Further, in step 5 and 8, the selection range of switch state is 18 state space vectors.
Compared with prior art, the present invention has following technical effect:
The present invention is based on id=0 control principle decouples the Nonlinear state space model of durface mounted permanent magnet synchronous motor For two linear state-space submodels.Based on the two submodels, two linear finite aggregate model predictive controllers are devised. In this way, primary nonlinear Model Predictive Control problem can make can control by two linear finite aggregate model predictive controllers It solves, avoids the solution of nonlinear problem, greatly reduce amount of calculation, improve the real-time of control；It is real simultaneously The high dynamic performance of motor is showed；The selection range of switch state becomes 18 state spaces arrow from 8 traditional state space vectors Amount, reduces the harmonic wave of magnetic linkage, reduces the torque pulsation of motor.
Detailed description of the invention
Fig. 1 is finite aggregate Model Predictive Control principle；
Fig. 2 is traditional alternative switch state of finite aggregate Model Predictive Control；
Fig. 3 is 18 kinds of switch states of alternative inverter of each moment；
The Model Predictive Control Time-sharing control method schematic that Fig. 4 is studied for the present invention.
Specific embodiment
Below in conjunction with attached drawing, the present invention is further described:
A kind of Time-sharing control method of durface mounted permanent magnet synchronous motor time-sharing control system referring to FIG. 1 to FIG. 4, is based on A kind of durface mounted permanent magnet synchronous motor time-sharing control system, the system include that control module, inverter are synchronous with durface mounted permanent magnet Motor；Control module connects inverter, and inverter connects surface-mount type permanent magnet synchronous motor；Control module includes the first finite aggregate mould Type predictive controller and the second finite aggregate model predictive controller；First finite aggregate model predictive controller and the second finite aggregate mould Type predictive controller is connect by gating switch with inverter；First finite aggregate model predictive controller is same for durface mounted permanent magnet The rotating-speed tracking of motor is walked, the second finite aggregate model predictive controller is used for the i of durface mounted permanent magnet synchronous motordIt adjusts.
The following steps are included:
Step 1, controlled device nonlinear mathematical model is established, controlled device is inverter electricity synchronous with durface mounted permanent magnet Machine；
It step 2, is two linear submodels by the nonlinear model decoupling in step 1；
Step 3, sampling instant k setting enable signal be low level, enable the second finite aggregate model predictive controller into The i of row durface mounted permanent magnet synchronous motordIt adjusts；
Step 4, the current value at k+1 moment is predicted according to current time；
Step 5, it chooses so that the optimized switching state that the switch state that cost function minimizes should be taken as the k+1 moment Sabc_d；
Step 6, it is high level in sampling instant k+1 setting enable signal, enables the first finite aggregate model predictive controller Carry out rotating-speed tracking；
Step 7, the tachometer value at k+2 moment is predicted according to current time；
Step 8, it chooses so that the optimized switching state that the switch state that cost function minimizes should be taken as the k+2 moment Sabc_ω。
1. System describe and modeling
1.1 mathematical models are established
In finite aggregate Model Predictive Control, it should define cost function to find optimum control movement.In each control It in period, selects the switch state that can minimize cost function as optimum state, and is acted in next control period In inverter.In the present invention, we focus on that the height for how choosing switch state to realize durface mounted permanent magnet synchronous motor is dynamic State property energy, therefore we need to model two-level inverter and durface mounted permanent magnet synchronous motor.
1.1.1 two-level inverter models
We use vector S=[Sa, Sb, Sc]TTo indicate the switch state of two-level inverter.The switch shape of every half-bridge State can be expressed as follows:
X represents certain phase bridge arm, can take A, B or C.In d-q coordinate system, by using the switch state function, we Available stator voltage and two-level inverter switch state relationship are as follows:
Wherein,
Here VdAnd VqIt is d axis and q axis stator voltage, VdcIt is DC bus-bar voltage, θ is rotor electrical angle.It is being proposed Finite aggregate model predictive control method in, selected switch state S will as optimum control act.
1.1.2 durface mounted permanent magnet synchronous motor models
The modeling of durface mounted permanent magnet synchronous motor is assumed based on the following: ignoring core saturation, lag and eddy-current loss and makes At influence and external condition (such as temperature and humidity) to motor operation when permanent magnet flux linkage influence.And think by forever The magnetic field that magnet generates is always sinusoidal.Under these conditions, in d-q coordinate system durface mounted permanent magnet synchronous motor mathematical model It is as follows:
Here id, iq, Vd, VqThe stator current and voltage in d-q coordinate system are respectively represented, L is fixed in d-q coordinate system The equivalent inductance of sub- winding, R are stator resistance, ψfIt is permanent magnet flux linkage, pnIt is number of pole-pairs, ωmIt is rotor machinery angular speed.
Torque equation and mechanical motion equation are expressed as follows:
TeIt is electromagnetic torque, J is motor rotary inertia, TLIt is load torque.
The form that (3)-(5) are write as state-space expression is as follows:
From (6), it can be seen that, state-space model includes nonlinear terms ωmidAnd ωmiq.Therefore having based on this model Limit collection Model Predictive Control will be nonlinear Model Predictive Control, need to solve using nonlinear method, and finite aggregate mould There is presently no effective solutions for type PREDICTIVE CONTROL nonlinear problem.
The decoupling of 1.2 nonlinear models is two linear submodels
I is used in the present inventiond=0 control strategy realizes the target of durface mounted permanent magnet synchronous motor rotating-speed tracking, because There are two control target for this finite aggregate Model Predictive Control tool.Main target is that motor actual speed is made to reach reference value, and Two objects are that d shaft current is remained 0.The two targets can be come real by using two finite aggregate model predictive controllers It is existing.First controller is responsible for rotating-speed tracking, therefore its prediction model should be by iqAnd ωmAs state variable, second controller It is responsible for idZero is remained, therefore its prediction model should be by idIt is considered as state variable.
1.2.1iq-ωmLinear submodel
In nonlinear model (6), if selection iqAnd ωmAs state variable, and assume id=0, then the model becomes The state-space model of one linearly invariant, it may be assumed that
By (7) further discretization and by S1=[Sa1, Sb1, Sc1]TAs input variable, submodel (7) be can be written as:
Output equation are as follows:Wherein
TsIt is the sampling period, k indicates sampling instant.
In this equation, output variable is ωm.Linear submodel (8) can use the linear finite aggregate model of standard PREDICTIVE CONTROL realizes ωmIt adjusts.
1.2.2idLinear submodel
Similarly, in (6), if by idIt is set as state variable, and by iqAnd ωmIt is considered as constant, then nonlinear model (6) become another linear state-space submodel:
(10) can further discretization, and work as switch state S2=[Sa2, Sb2, Sc2]TAs two-level inverter When input variable:
Output equation are as follows:Herein,
Output variable is i hereind.Obviously submodel (11) is also linearly invariant model.
Up to the present, nonlinear dynamical model (6) decoupling of durface mounted permanent magnet synchronous motor is two lines by we Property time-invariant model (8) and (11), and use the switch state of inverter as the input variable of two submodels.In this way Nonlinear Model Predictive Control problem can be converted into two Linear Model for Prediction control problems, this can be limited by two The timesharing of collection model predictive controller enables to solve.
2 Time-sharing control methods
As in the previous section, the present invention needs to realize two control targets, i.e. rotating-speed tracking and d shaft current is adjusted.? In the present invention, the two control targets are realized by alternately enabled two finite aggregate model predictive controllers.
2.1 are used for the first finite aggregate model predictive controller of rotating-speed tracking；
As described above, our control target is realized by alternately enabled two finite aggregate model predictive controllers 's.Currently designed first the first finite aggregate model predictive controller is used to be responsible for rotating-speed tracking.
First finite aggregate model predictive controller is based on iq-ωmLinear submodel (8)-(9) are designed.Due to controlling mesh Mark is rotating-speed tracking, at every sampling moment k, and the cost function of the first finite aggregate model predictive controller can be designed as:
Y herein1(k)=[y1(k+1|k),y1(k+2|k),…,y1(k+H|k)]T,Yref1(k)=[yref1(k+1|k), yref1(k+2|k),…,yref1(k+H|k)]T, (k+h | k) indicates the value at the k+h moment predicted based on k moment available information.U1 (k) control sequence when moment k, i.e. U are indicated1(k)=[u1(k+1),u1(k+2),…,u1(k+H)]T。Wβ1=diag (β1)and W1-β1=diag (1- β1) be two optimization aims weight matrix.For the sake of simplicity, it predicts and controls time domain and be all set as H.In generation In valence function, the predicted value y of output can be calculated according to state-space model (9)1(k+h|k).Output valve y is predicted in this way1(k+h | k) about switch state [u1(k),u1(k+1),…,u1(k+H)]TFunction expression it is as follows:
y1(k+h | k)=C1A1 hx1(k)+C1A1 h-1(B1u1(k+1)+E1z1(k))
+C1A1 h-2(B1u1(k+2|k)+E1z1(k+1|k))+C1A1 1(B1u1(k+h|k)+E1z1(k+h-1|k)) (14)
Y1(k) it is expressed as follows in the matrix form:
Wherein
In practical applications, it is contemplated that the output capacity of two-level inverter, so should add to output voltage and electric current With limitation.We apply following linear inequality constraint:
-iq,max≤iq(k+h|k)≤iq,max, h=1,2 ..., H (16)
-Vq,max≤Vq(k+h|k)≤Vq,max, h=1,2 ..., H (17)
By above-mentioned derivation, the linear representation of the first finite aggregate model predictive controller can be indicated with matrix form Are as follows:
s.t.L1U1(k)≤K1(19)
Herein,
L1And K1It is related linear inequality constraint matrix with (16) and (17).
2.2 are used for idThe the second finite aggregate model predictive controller adjusted
In this trifle, the second finite aggregate model predictive controller of design is responsible for idIt adjusts.Second finite aggregate model prediction Controller is based on idLinear submodel (11)-(12) be designed.Design method and the first finite aggregate model predictive controller It is similar.K at every sampling moment, the cost function design of the second finite aggregate model predictive controller are as follows:
Voltage and current inequality constraints is as follows:
-id,max≤id(k+h|k)≤id,max, h=1,2 ..., H (23)
-Vd,max≤Vd(k+h|k)≤Vd,max, h=1,2 ..., H (24)
Then the second finite aggregate model predictive controller problem is described as follows in the matrix form:
s.t.L2U2(k)≤K2(26)
Wherein,
L2And K2It is linear inequality constraint matrix relevant to (23) and (24).
2.3 two finite aggregate model predictive controllers carry out Time-sharing control
We use vector Sabc_d=[Sa_d,Sb_d,Sc_d]TAnd Sabc_ω=[Sa_ω,Sb_ω,Sc_ω]TIt indicates respectively The optimum control movement being calculated by the first finite aggregate model predictive controller and the second finite aggregate model predictive controller.But It is at each control moment, only one of those can be applied on inverter.In order to solve this problem, the present invention proposes A kind of Time-sharing control strategy, it is intended to realize two different control targets by alternately enabled two controllers.
The Time-sharing control method schematic diagram proposed is as shown in Figure 4.Basic thought is as follows: when enable signal is in low level When, enable the second finite aggregate model predictive controller, and application switch state Sabc_dTo inverter, to control the d axis of motor Electric current is 0.Conversely, the first finite aggregate model predictive controller is enabled, switch state when enable signal is high level Sabc_ωIt is applied to inverter, so that controlling motor speed follows reference locus.That is, real by Time-sharing control signal The alternating for having showed two finite aggregate model predictive controllers is enabled.In this way in moment k, d shaft current is controlled near 0, then In moment k+1, so that it may think that d axis and q shaft current are full decoupled.It ensure that the accuracy of state space submodel (7), this It is extremely important to the first finite aggregate model predictive controller.
First finite aggregate model predictive controller and the second finite aggregate model predictive controller have different control targets, Therefore when one of them is enabled, control action may be unfavorable to another control target.On the one hand when the second finite aggregate When model predictive controller works, revolving speed will be reduced.Similarly, when the first finite aggregate model predictive controller works, d axis electricity Stream may be out of control and may be deviateed near 0, therefore the length of each controller runing time is to pass for dynamic property Important.On the other hand, the excessively high electric current that may cause of the enabled frequency of two finite aggregate model predictive controllers is neither 0, together When motor speed cannot achieve tracking.Therefore the frequency of control signal is also critically important.Therefore, the setting of Time-sharing control enable signal Need to pay attention to action time and signal frequency.
In short, the rotating-speed tracking of durface mounted permanent magnet synchronous motor can pass through the first finite aggregate model predictive controller and The Time-sharing controls of two finite aggregate model predictive controllers is realized.That is, original Nonlinear Model Predictive Control problem It is resolved by two Linear Model for Prediction control problems.
3,18 state space vectors
Fig. 2 is 8 kinds of traditional space voltage vectors, if 6 useful space vectors of each period successively act on once, I.e. every the angulation change one action state of π/3, and keep switch state constant in the angle of π/3, then the magnetic linkage constituted is six Clap staircase waveform.Obviously, the magnetic linkage harmonic component that 6 effective vector states are formed is greater than shape after traditional state space vectors modulation At magnetic linkage harmonic wave, this will lead to biggish torque pulsation.It, generally can be using raising inverter in order to reduce torque pulsation Switching frequency.However will increase the computation burden of controller in this way, switching loss is also bigger.So in order to reduce harmonic component, The present invention uses the control method of interblock space vector.A cycle is divided into 6 sectors by 6 of-state voltage space vectors, that is, is existed Several space vectors are inserted into achieve the purpose that reduce harmonic component in each sector.Such as V is utilized in I sector1、V2Synthesize V12Arrow Amount, the interblock space vector V newly formed12Phase and V1、V2Vector is different, but amplitude is identical.It similarly can also be at other V is inserted into sector respectively23、V34、V45、V56、V61.Since each interblock space vector is made of two spaces vector, substantial structure At be 18 states space vector of voltage.The latter vector should be placed on front by the order of combined vectors, and will be previous Vector is put behind, such as V1、V12、V2The order of expansion is V1、V2、V1、V2.By the combined vectors head and the tail phase of 18 above-mentioned states Polygon shown in Fig. 3 can be depicted as by connecing, it is clear that it shows that its waveform harmonics component will be less than 6 states sky closer to circle Between vector harmonic wave, this is beneficial to the reduction of motor torque ripple, while can reduce switching frequency.

Claims (7)

1. a kind of durface mounted permanent magnet synchronous motor time-sharing control system, which is characterized in that including control module, inverter and Surface Mount Formula permanent magnet synchronous motor；Control module connects inverter, and inverter connects surface-mount type permanent magnet synchronous motor；Control module includes the One finite aggregate model predictive controller and the second finite aggregate model predictive controller；First finite aggregate model predictive controller and Two finite aggregate model predictive controllers are connect by gating switch with inverter；First finite aggregate model predictive controller is used for table The rotating-speed tracking of mounted permanent magnet synchronous motor, the second finite aggregate model predictive controller are used for the i of durface mounted permanent magnet synchronous motord It adjusts.
2. a kind of Time-sharing control method of durface mounted permanent magnet synchronous motor time-sharing control system, which is characterized in that wanted based on right A kind of durface mounted permanent magnet synchronous motor time-sharing control system described in asking 1, comprising the following steps:
Step 1, controlled device nonlinear mathematical model is established, controlled device is inverter and durface mounted permanent magnet synchronous motor；
It step 2, is two linear submodels by the nonlinear model decoupling in step 1；
Step 3, it is low level in sampling instant k setting enable signal, enables the second finite aggregate model predictive controller and carry out table The i of mounted permanent magnet synchronous motordIt adjusts；
Step 4, the current value at k+1 moment is predicted according to current time；
Step 5, it chooses so that the optimized switching state that the switch state that cost function minimizes should be taken as the k+1 moment Sabc_d；
Step 6, it is high level in sampling instant k+1 setting enable signal, enables the first finite aggregate model predictive controller and carry out Rotating-speed tracking；
Step 7, the tachometer value at k+2 moment is predicted according to current time；
Step 8, it chooses so that the optimized switching state that the switch state that cost function minimizes should be taken as the k+2 moment Sabc_ω。
3. a kind of Time-sharing control method of durface mounted permanent magnet synchronous motor time-sharing control system according to claim 2, It is characterized in that, in step 1, mathematical model includes:
1) two-level inverter models:
Use vector S=[Sa, Sb, Sc]TTo indicate the switch state of two-level inverter；The switch state of every half-bridge can indicate It is as follows:
X represents certain phase bridge arm, takes A, B or C；In d-q coordinate system, by using the switch state function, stator voltage is obtained It is as follows with two-level inverter switch state relationship:
Wherein,
Here VdAnd VqIt is d axis and q axis stator voltage, VdcIt is DC bus-bar voltage, θ is rotor electrical angle；Selected switch shape State S will be acted as optimum control；
2) durface mounted permanent magnet synchronous motor models:
Permanent magnetism when ignoring influence and external temperature caused by core saturation, lag and eddy-current loss and humidity to motor operation The influence of body magnetic linkage；And think that the magnetic field that is generated by permanent magnet is always sinusoidal, under these conditions, table in d-q coordinate system The mathematical model of mounted permanent magnet synchronous motor is as follows:
Here id, iq, Vd, VqThe stator current and voltage in d-q coordinate system are respectively represented, L is stator winding in d-q coordinate system Equivalent inductance, R is stator resistance, ψfIt is permanent magnet flux linkage, pnIt is number of pole-pairs, ωmIt is rotor machinery angular speed；
Torque equation and mechanical motion equation are expressed as follows:
TeIt is electromagnetic torque, J is motor rotary inertia, TLIt is load torque；
The form that (3)-(5) are write as state-space expression is as follows:
From (6), it can be seen that, state-space model includes nonlinear terms ωmidAnd ωmiq。
4. a kind of Time-sharing control method of durface mounted permanent magnet synchronous motor time-sharing control system according to claim 2, It is characterized in that, in step 2, decoupling includes the following:
1)iq-ωmLinear submodel
In nonlinear model (6), i is selectedqAnd ωmAs state variable, and assume id=0, then the model becomes one linearly When constant state-space model, it may be assumed that
By (7) further discretization and by S1=[Sa1, Sb1, Sc1]TAs input variable, submodel (7) be can be written as:
Output equation are as follows:
Wherein TsIt is the sampling period, k indicates sampling instant；
In this equation, output variable is ωm；Linear submodel (8) using the linear finite aggregate Model Predictive Control of standard come Realize ωmIt adjusts；
2)idLinear submodel
In (6), by idIt is set as state variable, and by iqAnd ωmIt is considered as constant, then it is linear to become another for nonlinear model (6) State space submodel:
(10) further discretization, and work as switch state S2=[Sa2, Sb2, Sc2]TInput variable as two-level inverter When:
Output equation are as follows:
Herein,
Output variable is i hereind；Submodel (11) is also linearly invariant model.
5. a kind of Time-sharing control method of durface mounted permanent magnet synchronous motor time-sharing control system according to claim 2, It is characterized in that, in step 6, the first finite aggregate model predictive controller is based on iq-ωmLinear submodel (8)-(9) set Meter；K+1 at every sampling moment, the cost function design of the first finite aggregate model predictive controller are as follows:
Y herein1(k)=[y1(k+1|k),y1(k+2|k),…,y1(k+H|k)]T,Yref1(k)=[yref1(k+1|k),yref1 (k+2|k),…,yref1(k+H|k)]T, (k+h | k) indicates the value at the k+h moment predicted based on k moment available information；U1(k) table Show control sequence when moment k, i.e. U1(k)=[u1(k+1),u1(k+2),…,u1(k+H)]T；Wβ1=diag (β1)and W1-β1 =diag (1- β1) be two optimization aims weight matrix；Prediction and control time domain are all set as H；In cost function, according to State-space model (9) calculates the predicted value y of output1(k+h|k)；Output valve y is predicted in this way1(k+h | k) about switch state [u1(k),u1(k+1),…,u1(k+H)]TFunction expression it is as follows:
y1(k+h | k)=C1A1 hx1(k)+C1A1 h-1(B1u1(k+1)+E1z1(k))
+C1A1 h-2(B1u1(k+2|k)+E1z1(k+1|k))+C1A1 1(B1u1(k+h|k)+E1z1(k+h-1|k)) (14)
Y1(k) it is expressed as follows in the matrix form:
Wherein
In practical applications, it is contemplated that the output capacity of two-level inverter, so should be limited output voltage and electric current System；We apply following linear inequality constraint:
-iq,max≤iq(k+h|k)≤iq,max, h=1,2 ..., H (16)
-Vq,max≤Vq(k+h|k)≤Vq,max, h=1,2 ..., H (17)
By above-mentioned derivation, the linear representation of the first finite aggregate model predictive controller can be indicated with matrix form are as follows:
s.t.L1U1(k)≤K1 (19)
Herein,
L1And K1It is related linear inequality constraint matrix with (16) and (17).
6. a kind of Time-sharing control method of durface mounted permanent magnet synchronous motor time-sharing control system according to claim 2, It is characterized in that, in step 3, the second finite aggregate model predictive controller is based on idLinear submodel (11)-(12) be designed； K at every sampling moment, the cost function design of the second finite aggregate model predictive controller are as follows:
Voltage and current inequality constraints is as follows:
-id,max≤id(k+h|k)≤id,max, h=1,2 ..., H (23)
-Vd,max≤Vd(k+h|k)≤Vd,max, h=1,2 ..., H (24)
Then the second finite aggregate model predictive controller problem is described as follows in the matrix form:
s.t.L2U2(k)≤K2 (26)
Wherein,
L2And K2It is linear inequality constraint matrix relevant to (23) and (24).
7. a kind of Time-sharing control method of durface mounted permanent magnet synchronous motor time-sharing control system according to claim 2, It is characterized in that, the selection range of switch state is 18 state space vectors in step 5 and step 8.
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