CN108599656A - Hybrid electric vehicle switching magnetic-resistance BSG control system without position sensor and method - Google Patents
Hybrid electric vehicle switching magnetic-resistance BSG control system without position sensor and method Download PDFInfo
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- CN108599656A CN108599656A CN201810390858.1A CN201810390858A CN108599656A CN 108599656 A CN108599656 A CN 108599656A CN 201810390858 A CN201810390858 A CN 201810390858A CN 108599656 A CN108599656 A CN 108599656A
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
- H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
- H02P21/14—Estimation or adaptation of machine parameters, e.g. flux, current or voltage
- H02P21/18—Estimation of position or speed
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
- H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
- H02P21/0003—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control
- H02P21/0017—Model reference adaptation, e.g. MRAS or MRAC, useful for control or parameter estimation
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
- H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
- H02P21/24—Vector control not involving the use of rotor position or rotor speed sensors
- H02P21/26—Rotor flux based control
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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
- 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
- H02P25/08—Reluctance motors
- H02P25/086—Commutation
- H02P25/089—Sensorless control
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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
- H02P2203/00—Indexing scheme relating to controlling arrangements characterised by the means for detecting the position of the rotor
- H02P2203/03—Determination of the rotor position, e.g. initial rotor position, during standstill or low speed operation
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- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Control Of Electric Motors In General (AREA)
- Control Of Motors That Do Not Use Commutators (AREA)
Abstract
The present invention discloses a kind of hybrid electric vehicle switching magnetic-resistance BSG control system without position sensor and method, to phase current i1, rotor angletAnd magnetic linkage ψtData are equally divided into forecast sample data and training sample data, by the input sample that training sample is least square method supporting vector machine, obtain least square method supporting vector machine prediction model；The width cs of regularization parameter γ and Radial basis kernel function in model are optimized using ant group algorithm, obtain final mask；Acquire the transient current i of BSGs0, by removing the resume module output current i that makes an uproarstIt is ψ with magnetic linkagest, by electric current istWith magnetic linkage ψstIt substitutes into final mask and obtains prediction bits angle setting θstThe control of position-sensor-free rotating speed is realized to BSG；Achieve the purpose that position-sensorless control using finite element analysis and based on ant colony least square method supporting vector machine method is improved, improves the stability of control.
Description
Technical field
The present invention relates to the belt driven starter generator of hybrid vehicle (hereinafter referred to as BSG) systems, specifically
The control system without position sensor and method of the BSG systems are suitable for the high performance control to BSG systems, belong to BSG mixing
Dynamic Control field.
Background technology
Hybrid vehicle has energy saving and discharge superiority.BSG is that a kind of having idle stop and startup function
Technology of Hybrid Electric Vehicle uses belt transmissioning mode to be mixed into action edge, oil consumption and discharge when can reduce vehicle idling work.When
When automobile normal running, BSG is identical as traditional generator operation principle, is generated electricity, is charged the battery by driven by engine；Work as vapour
BSG can make engine break-off when vehicle stops；When vehicle is started to walk again, BSG systems quickly start up engine, eliminate
Oil consumption, discharge and noise of the engine in idling work.Make garage rise during sailing by BSG technologies to stop, together
When can also be achieved Brake energy recovery, not only reduce Fuel consumption, but also promote the emission level of vehicle.
It is accurate to determine that the position of rotor is very in order to control hybrid vehicle switching magnetic-resistance BSG turn-on angles and shutdown angle
Important.In order to determine the position of rotor, electromagnetic sensor or optical sensor are generally used in traditional control system, but
Electromechanics size can be increased by being these sensors, and secondly sensor can also reduce the security performance of system, while will produce and make an uproar
Sound；Itself with regard to sensor, susceptibility variation with temperature and have a greater change so that motor control stability under
Drop.In order to improve the stability of control, need to control using the speed of no sensor and position.
Invention content
It is proposed the invention aims to accurately determine the position of the switching magnetic-resistance BSG rotors of existing hybrid vehicle
A kind of high performance control system without position sensor and its control method.
The technical solution that hybrid electric vehicle switching magnetic-resistance BSG control system without position sensor of the present invention uses
It is：By control module, power conversion modules, signal measurement module, except module of making an uproar, prediction module and rotation speed measuring module group
At；
It is described except make an uproar module input be acquisition BSG transient current is0With feedback phase voltage vs0, output be magnetic
Chain ψstWith electric current ist, except the output end for module of making an uproar is separately connected prediction module and control module, the output end connection of prediction module
Control module；
The prediction module input is magnetic linkage ψst, electric current istWith the phase current i of BSG1, output be prediction bits angle setting
θst；
The transient speed n that BSG is measured using rotation speed measuring module, by transient speed n and reference velocity nrefIt compares to obtain
Speed error Δ e；
The control module input is speed error Δ e, rotor prediction bits angle setting θstWith electric current ist, control module
Output is voltage signal V, and voltage signal V exports control voltage V through power conversion moduless, the output end of power conversion modules
It is separately connected BSG and signal measurement module, signal measurement module detects the feedback phase voltage vs0。
Further, the prediction module by emulation module, data processing module, least square method supporting vector machine model and
Ant group algorithm optimization module forms；
The emulation module carries out finite element simulation modeling to BSG, obtains phase current i1, magnetic linkage ψ1And rotor angle1；
The data processing module is to magnetic linkage ψ1And rotor angle1Rotor angle after obtaining exclusive PCR data into processing
Spend θtWith magnetic linkage ψt；
The phase current i1, rotor anglet, magnetic linkage ψt, magnetic linkage ψst, electric current istCollectively as least square support to
The input of amount machine model, the output of least square method supporting vector machine model is the prediction bits angle setting θst；
The ant group algorithm optimization module is to the regularization parameter γ and radial direction base core in least square method supporting vector machine
The width cs of function optimize.
What the control method of hybrid electric vehicle switching magnetic-resistance BSG control system without position sensor of the present invention used
Technical solution is to comprise the steps of：
A, finite element simulation is carried out to BSG to model to obtain phase current i1, magnetic linkage ψ1And rotor angle1, the magnetic linkage that will obtain
ψ1And rotor angle1The exclusive PCR data in data processing module；
B, to phase current i1, rotor angletAnd magnetic linkage ψtData be equally divided into forecast sample data and training sample
The input sample that training sample is least square method supporting vector machine is obtained least square method supporting vector machine prediction model by data；
The width cs of regularization parameter γ and Radial basis kernel function in model are optimized using ant group algorithm, obtain final mask；
C, the transient current i of BSG is acquireds0, processing output current i is carried out by removing module of making an uproarstIt is ψ with magnetic linkagest, will be electric
Flow istIt is ψ with magnetic linkagestIt substitutes into final mask, calculates the prediction bits angle setting θst。
D, the final control voltage V of power inverter outputs, the control of position-sensor-free rotating speed is realized to BSG.
The advantages of highlighting, is after the present invention uses above-mentioned technical proposal：
1, control method of the invention need not use electromagnetic sensor or optical sensor, reduce motor cost and
Mechanical dimension, improves the security performance of system, while reducing noise.
2, magnetic linkage and rotor position data are obtained by finite element simulation, the data gone out using FEM calculation, are transported to motor
Rotating speed when row is predicted there is higher accuracy with position, not only increases the reliable journey of support vector machines training data
Degree, and timeliness is high, improves the accuracy and development efficiency of control.
3, itself with regard to sensor, susceptibility variation with temperature and have a greater change so that motor control
Stability declines.The present invention is controlled using the speed of no sensor and position, most using finite element analysis and based on improvement ant colony
Small two multiply support vector machine method to achieve the purpose that position-sensorless control, improve the stability of control.
Description of the drawings
Fig. 1 is the structural frames of hybrid vehicle switching magnetic-resistance BSG control system without position sensor of the present invention
Figure；
Fig. 2 is the internal structure of prediction module and its external block diagram with BSG, except module of making an uproar in Fig. 1；
Fig. 3 is that transient current removes process of making an uproar in Fig. 2.
Specific implementation mode
As shown in Figure 1, hybrid vehicle switching magnetic-resistance BSG control system without position sensor of the present invention is by control mould
Block, power conversion modules, signal measurement module, except module of making an uproar, prediction module and rotation speed measuring module form.
The transient speed n that BSG is measured using rotation speed measuring module, by transient speed n and reference velocity nrefIt compares to obtain
Speed error Δ e, speed error Δ e are input to control module, and control module output is voltage signal V, control module it is defeated
Outlet connects the input terminal of power conversion modules, and voltage signal V is input in power conversion modules, power conversion modules output
It is final control voltage Vs, the output end of power conversion modules is separately connected BSG and signal measurement module, by controlling voltage
VsThe operation of BSG is controlled, and will control voltage VsIt is input in signal measurement module.The output end connection of signal measurement module removes
It makes an uproar module, signal measurement module detects the voltage at a certain moment of final control voltage Vs, that is, feeds back phase voltage vs0, and should
Feed back phase voltage vs0It is input to except in module of making an uproar.
Acquire the transient current i at BSG a certain moments0, and by transient current is0It is input to except module of making an uproar, except module pair of making an uproar
Feed back phase voltage vs0With transient current is0It is handled, output magnetic linkage ψstWith electric current ist.Except the output end for module of making an uproar is separately connected
Prediction module and control module, the output end of prediction module also link control module, except module of making an uproar is by magnetic linkage ψstWith electric current istAltogether
With being input in prediction module, by electric current istIt is input in control module.
Finite element modeling is carried out to switching magnetic-resistance BSG, and collects phase current i1, by phase current i1It is input to prediction module
In, phase current i of the prediction module to input1, magnetic linkage ψstWith electric current istIt is handled, obtains rotor prediction bits angle setting θst, and will
Rotor prediction bits angle setting θstIt is input to control module.Rotor prediction bits angle setting θ of the control module to inputst, electric current istAnd rotating speed
Error delta e processing obtains voltage signal V.
As shown in Fig. 2, prediction module is by emulation module, data processing module, least square method supporting vector machine model and ant
Group's algorithm optimization module composition.Emulation module carries out finite element simulation modeling to BSG, obtains phase current i1, magnetic linkage ψ1And rotor angle
Spend θ1.Data processing module is to magnetic linkage ψ1And rotor angle1It is handled, exclusive PCR data have obtained exclusive PCR data
Rotor angle afterwardstAnd magnetic linkage ψt.By phase current i1, rotor angletAnd magnetic linkage ψt, except make an uproar module output magnetic linkage ψst
With electric current istCommon input least square method supporting vector machine model, the output of least square method supporting vector machine model is to turn in real time
Sub- prediction bits angle setting θst.Ant group algorithm optimization module is to the regularization parameter γ and radial direction base core in least square method supporting vector machine
The width cs of function optimize, and optimization is obtained the regularization parameter γ of optimized parameter1With the width cs of Radial basis kernel function1It is defeated
Enter least square method supporting vector machine model.
It is first when hybrid vehicle switching magnetic-resistance BSG control system without position sensor of the present invention works referring to Fig. 1-2
First prediction module is learnt, finite element modeling first is carried out to hybrid vehicle switching magnetic-resistance BSG, is really being sized
After material, to having, the electromagnetism under load or no-load operating mode is distributed and torque calculates.Acquire hybrid vehicle switch
Magnetic linkage data of the magnetic resistance BSG under different electric currents and rotor-position, wherein never aligned position is every to aligned position for rotor-position
It is calculated once every 1 °, the data of acquisition is input in prediction module, after the finite element simulation of emulation module, obtain phase current
i1, magnetic linkage ψ1And rotor angle1The three-dimensional data of relationship.
The magnetic linkage ψ that will then obtain1And rotor angle1It is input in data processing module, and by phase current i1Input is most
Small two multiply supporting vector machine model.Data processing module is used for exclusive PCR data, since sampled data frequency is sufficiently high,
Gap between adjacent sample values should very little, if the estimated value of first-order difference with sampled value has larger gap and more than certain
Threshold value, then judge sampled value for interference value, be used in combination estimated value replace sampled value.This method not only improves the accuracy of data,
And data processing method is simple, efficient.Using first difference method to each magnetic linkage ψ1Data and rotor angle1Data carry out
Processing, with magnetic linkage ψ1Data instance, applying equation (1) predict m-th of magnetic linkage：
In formula,For the estimated value of m-th of magnetic linkage.By the predicted value of magnetic linkageWith the magnetic linkage after corresponding finite element analysis
ψmIt is compared, discriminates whether as interference value：
When formula (2) are set up, i.e., FEM calculation goes out magnetic linkage ψmGap between the estimated value of first-order difference is more than
When threshold epsilon (threshold epsilon according to certain rule settings, usually the integral multiple of standard deviation), indicate that the point data is interference value, then
The point data can be rejected, estimated value is used in combinationIt is replaced.It rejects the magnetic linkage after interference and is denoted as ψt.Similarly by rotor angle1
It is handled to obtain the rotor position after rejecting interferencet.Reject the magnetic linkage ψ after interferencetRotor position after being interfered with rejectingt
It is input to least square method supporting vector machine model, by rotor positiontIt is input in ant group algorithm optimization module.
To phase current i1, rotor angletAnd magnetic linkage ψtData be equally divided into L group forecast sample data and L groups training
Sample data.L group training samples are (x1,y1),…,(x2, y2) ..., (xL, yL) it is input sample, wherein xj=(i1,ψtj),j
=1.2.3 ... L, ψtjFor j-th of training sample magnetic linkage for rejecting after interfering；yj=θtj, θtjTo reject j-th of instruction after interfering
Practice sample rotor-position, in feature space, least square method supporting vector machine uses such as minor function：
F (x)=ωTφ(x)+b (3)
Wherein, ω is weight vector, and T is transposition, and b is offset, and Ф is a Nonlinear Mapping.Definition：
yj=ωTφ(xj)+b+τj (5)
Wherein J is optimization object function, and γ is regularization parameter, τjFor relaxation factor.
It is as follows to construct Lagrangian：
Wherein ajFor Lagrange multiplier.
It can be obtained according to KKT (Karush-Kuhn-Tucker) optimal condition：
Eliminate ω and τjAfter solve the optimization problem can be converted to solve following linear equation group：
Wherein A=[a1 a2 … aL]T, Y=[y1 y2 … yL]T, IL*1=[1 11 ... 1]T, I=diag 1,1,
1 ..., 1 }, k=1,2,3 ..., L.Take kernel function：
Wherein, K is kernel function, and σ is the width of Radial basis kernel function.
Finally obtaining the decision function of least square method supporting vector machine recurrence is：
Wherein ajIt can be solved by formula (11) with b.
I.e. the prediction model of initial position of rotor can must be：
I, ψ are the current variable actually entered and the magnetic linkage variable that actually enters.
After obtaining the prediction model of initial position of rotor, then parameter optimization carries out it using ant group algorithm, optimization is regular
Change the width cs of parameter γ and Radial basis kernel function.When optimization, first, optimization aim is established：
Min Q indicate the minimum value of object function, θt2jFor the output quantity of forecast sample data, θojIt indicates forecast sample
Output quantity when data input quantity is input in model.
Take ant number ant=30, pheromones residual degree λ=0.7, cycle-index Q=500.Secondly, ant is set
Initial position, each position corresponds to one group of parameter (γ, σ) of least square method supporting vector machine, by the object function meter defined
The fitness value of individual is calculated, then passes through Δ ε=e- Q (γ, σ)Calculate the pheromone concentration of each ant.Wherein, Δ ε indicates every
Pheromone concentration when ant is initial.
25 ants are randomly selected in population, according to the pheromone concentration size of every ant position, are found out most
The position of excellent ant is set as Xbest, using it as target individual Xobj.Non-optimal ant is pressed in population moves to target ant position
Carry out global search.And the ant in optimal solution scans in close region as the following formula.After crossing one cycle, each ant position
It sets to optimal ant position and is moved, while updating every ant pheromone concentration ε (j+1)=(1- ρ) ε after movement
(j)+Δ ε, wherein ρ are pheromones volatilization number, and ε (j+1) is pheromone concentration after update, and ε (j) is update propheromone, prepheromone concentration.
Optimal ant position at the most dense position of pheromones is preserved.
The optimal solution that ant group algorithm is found in order to prevent is the optimal solution of part, is changed to traditional ant group algorithm
Into.Optimal ant position is preserved after fresh information element concentration each time, optimal location at this time is only in order to prevent
Local optimal location, rather than global optimal location, after each fresh information element concentration with 50 percent it is general
Rate resets the position of non-optimal ant in global scope.The pheromone concentration of all ants is updated in next iteration, weight
Pheromones thick is newly set as optimal ant position.This method is simply easily realized, and optimal solution has faster convergence rate
With higher precision.
Judge whether that reaching iterations 500 or target function type (11) is less than 0.001, if satisfied, then iteration terminates
Export optimized parameter (γ1, σ1), and substitute into formula (11) calculate optimization after offset b ' and optimization after Lagrange multiplier
aj', then the final mask of rotor-position is：
It is completed as previously described the study of prediction module.
After completing prediction module study, a certain moment transient current i of BSG are acquireds0, and by a certain moment transient current
is0It is handled by removing module of making an uproar.Processing mode is as shown in figure 3, concrete principle and flow are as follows：
Phase current i through being obtained in finite element simulation, with treated magnetic linkage ψtThere are following relationships：
{minψt, max ψt}=P (i) (17)
P (i) indicates the relational expression between phase current and magnetic linkage, min ψtIndicate magnetic linkage ψtMinimum value, max ψtIndicate magnetic linkage ψtMost
Big value, and magnetic linkage is obtained by following equation (18) during actually controlling.
ψs=∫ (vs-Ris)dt (18)
Wherein ψsFor in the magnetic linkage of line computation, R is phase resistance, vs0And isThe feedback phase voltage that respectively measures online and anti-
Present phase current.
A certain moment transient current is0Substitute into teachings { the min ψ that a certain moment magnetic linkage can be obtained in formula (17)0, max
ψ0, phase voltage v is fed back by a certain moment for exporting signal measurement modules0With a certain moment transient current is0It substitutes into (18)
It can be obtained in line computation magnetic linkage ψs0If in the magnetic linkage ψ of line computations0In teachings { the min ψ of magnetic linkage0, max ψ0In, then it represents that
Feed back phase current isIt is normal with voltage, except module of making an uproar will export the phase current i at the moments0With magnetic linkage ψs0.If in the magnetic of line computation
Chain ψs0Not in the teachings of magnetic linkage { min ψ0, max ψ0In, to reduce error, export last moment feedback current il0With magnetic linkage
ψl0, the electric current of final output is ist, the magnetic linkage of final output is ψst。
It is i by final output electric currentst, final output magnetic linkage is ψstIt substitutes into formula (16), you can obtain real-time rotor
Prediction bits angle setting θst。
Rotor prediction bits angle setting θst, except the electric current i after making an uproarstAnd speed error Δ e is input in control module, control
Module obtains the control voltage signal V of switching magnetic-resistance BSG using D2P as carrier, and control voltage signal V is input to power conversion
The motor speed control under position-sensor-free, the final control voltage V finally exported by power inverter are realized in devicesTo mixed
Power vehicle switching magnetic-resistance BSG is closed to be controlled.
Claims (7)
1. a kind of hybrid electric vehicle switching magnetic-resistance BSG control system without position sensor, it is characterized in that：By control module, power
Conversion module, signal measurement module, except module of making an uproar, prediction module and rotation speed measuring module form；
It is described except make an uproar module input be acquisition BSG transient current is0With feedback phase voltage vs0, output be magnetic linkage ψst
With electric current ist, except the output end for module of making an uproar is separately connected prediction module and control module, the output end connection control of prediction module
Module；
The prediction module input is magnetic linkage ψst, electric current istWith the phase current i of BSG1, output be prediction bits angle setting θst；
The transient speed n that BSG is measured using rotation speed measuring module, by transient speed n and reference velocity nrefIt compares to obtain rotating speed
Error delta e；
The control module input is speed error Δ e, rotor prediction bits angle setting θstWith electric current ist, control module output
It is voltage signal V, voltage signal V exports control voltage V through power conversion moduless, the output end of power conversion modules connects respectively
BSG and signal measurement module are met, signal measurement module detects the feedback phase voltage vs0。
2. hybrid electric vehicle switching magnetic-resistance BSG control system without position sensor according to claim 1, it is characterized in that：
The prediction module is by emulation module, data processing module, least square method supporting vector machine model and ant group algorithm optimization module
Composition；
The emulation module carries out finite element simulation modeling to BSG, obtains phase current i1, magnetic linkage ψ1And rotor angle1；
The data processing module is to magnetic linkage ψ1And rotor angle1Rotor angle after obtaining exclusive PCR data into processingt
With magnetic linkage ψt；
The phase current i1, rotor anglet, magnetic linkage ψt, magnetic linkage ψst, electric current istCollectively as least square method supporting vector machine mould
The input of type, the output of least square method supporting vector machine model is the prediction bits angle setting θst；
The ant group algorithm optimization module is to the regularization parameter γ and Radial basis kernel function in least square method supporting vector machine
Width cs optimize.
3. a kind of controlling party of hybrid electric vehicle switching magnetic-resistance BSG control system without position sensor as claimed in claim 2
Method, it is characterized in that comprising the steps of：
A, finite element simulation is carried out to BSG to model to obtain phase current i1, magnetic linkage ψ1And rotor angle1, the magnetic linkage ψ that will obtain1With turn
Subangle θ1The exclusive PCR data in data processing module；
B, to phase current i1, rotor angletAnd magnetic linkage ψtData be equally divided into forecast sample data and training sample data,
By the input sample that training sample is least square method supporting vector machine, least square method supporting vector machine prediction model is obtained；Using
Ant group algorithm optimizes the width cs of regularization parameter γ and Radial basis kernel function in model, obtains final mask；
C, the transient current i of BSG is acquireds0, processing output current i is carried out by removing module of making an uproarstIt is ψ with magnetic linkagest, by electric current ist
With magnetic linkage ψstIt substitutes into final mask, calculates the prediction bits angle setting θst。
D, the final control voltage V of power inverter outputs, the control of position-sensor-free rotating speed is realized to BSG.
4. control method according to claim 3, it is characterized in that：Least square method supporting vector machine prediction model isI, ψ is the current variable actually entered and reality
The magnetic linkage variable of input；L is training sample group number, j=1.2.3 ... L；ajFor Lagrange multiplier；K is kernel function；ψtjTo pick
Except j-th of training sample magnetic linkage after interference；B is offset.
5. control method according to claim 4, it is characterized in that：Least square method supporting vector machine final mask isB ' is offset after optimization；b’
aj' it is Lagrange multiplier after optimization.
6. control method according to claim 5, it is characterized in that：Using ant group algorithm to optimization regularization parameter γ and diameter
To base kernel function width cs when, the initial position of ant is set, and each position corresponds to one group of parameter (γ, σ).
7. control method according to claim 3, it is characterized in that：In step C, transient current is0With feedback phase voltage vs0Through
Formula { min ψt, max ψtTeachings { the min ψ of magnetic linkage are calculated in }=P (i)0, max ψ0, through formula ψs=∫ (vs-Ris) dt meters
Calculation obtains calculating magnetic linkage ψs0If online magnetic linkage ψs0In teachings { the min ψ of magnetic linkage0, max ψ0In, then it removes module of making an uproar and exports this
When electric current and magnetic linkage, electric current and magnetic linkage on the contrary then that export last moment；P (i) is the relational expression between phase current and magnetic linkage,
maxψt、minψtIt is magnetic linkage ψ respectivelytMaximum, minimum value.
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