CN104767449B - Self-bearings motors RBF neural adaptive inversion decoupling control and parameter identification method - Google Patents

Self-bearings motors RBF neural adaptive inversion decoupling control and parameter identification method Download PDF

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CN104767449B
CN104767449B CN201510092881.9A CN201510092881A CN104767449B CN 104767449 B CN104767449 B CN 104767449B CN 201510092881 A CN201510092881 A CN 201510092881A CN 104767449 B CN104767449 B CN 104767449B
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rbf neural
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CN104767449A (en
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孙宇新
钱忠波
朱熀秋
朱湘临
于焰均
乔薇
刘奕辰
杜怿
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Jiangsu University
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Jiangsu University
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Abstract

The invention discloses self-bearings motors RBF neural adaptive inversion decoupling control and parameter identification method, and composite controlled object is formed as an entirety by SVPWM modules, voltage source inverter, self-bearings motors and its load;The Adverse control and parameter identification to complex controll object are realized using two radial basis function neural networks;Adaptive inverse control device is formed by learning with RBF neural, before controller is serially connected in composite controlled object, inverse controller input is feedback signal and the error of Setting signal, thus forms closed-loop control;Again parameter adaptive identifier is formed by learning with a RBF neural, composite controlled object output quantity speed and displacement are recognized, realize and controlled without speed and without gap sensors, and estimation signal is helped into on-line study by learning algorithm, realize and self-bearings motors Dynamic Nonlinear Decoupling is controlled.It controls speed soon and identification precision is higher, and control system is excellent.

Description

Self-bearings motors RBF neural adaptive inversion decoupling control and parameter are distinguished Knowledge method
Technical field
The present invention is the self-bearings motors RBF neural Adaptive inverse control and ginseng of a kind of multivariable nonlinearity Number discrimination method, suitable for the high performance control of self-bearings motors.Self-bearings motors inherit magnetic bearing electricity Machine advantage, have the characteristics that without friction, without wear, be not required to lubricate and seal, sterile, pollution-free, long lifespan, be highly suitable for noting Enter high-speed precision digital control lathe, high pressure sealing pump, specialized robot, high speed gyro, satellite flywheel, high-speed aircraft and control dress Put, the high-technology field of the high-speed driving such as supercentrifuge, high-speed flywheel energy storage, application prospect is extensive, belongs to Electric Drive The technical field of control device.
Background technology
There is complicated electromagnetic relationship, therefore it is a kind of multivariable, non-linear, strong coupling inside self-bearings motors The controlled device of conjunction, to realize its radial displacement, rotating speed are accurately controlled it is extremely difficult.To realize to the asynchronous electricity of bearing-free The stable suspersion of motivation rotor and follow given rotating speed to run, must just carry out decoupling control to the torque force and suspending power of motor System.
But the control strategy of dynamic Decoupling Control of Load Torque is always the difficult point for realizing self-bearings motors steady operation. Common control has Air-gap-flux orientated and orientation on rotor flux, and experiment proves that both approaches can be to the asynchronous electricity of bearing-free Motivation realizes relatively stable control.Although Air-gap-flux orientated control method can be realized between electromagnetic torque and radial suspension force Decoupling control, but this algorithm is had a great influence by the parameter of electric machine (such as rotor resistance, rotor leakage inductance), and it is steady turn to exist Square, the scope of application are limited;Orientation on rotor flux method, can accomplish the decoupling between torque current and exciting current, but only There is rotor flux to reach and stablize and could be realized when keeping constant electromagnetic torque and rotor flux decoupling, belong to steady state decoupling not It can realize dynamic decoupling.BP neural network controls applied to self-bearings motors and obtains preferable control effect, but BP Neutral net in terms of function approximation there are convergence rate it is slow, be easily absorbed in local minimum the shortcomings of, and in theory with biological context It is not consistent.
To further improve the dynamic duty performance of self-bearings motors, it is necessary to consider self-bearings motors Dynamic decoupling and multivariable coordinated control are combined, and then Structure of need is compacter, the asynchronous electricity of the bearing-free of more excellent performance Motivation decoupling controller.
The country is existing to obtain related application:1) number of patent application CN20061038711.3, it is entitled:Bearing-less AC The control method of asynchronous motor neural network inverse decoupling controller, the invention patent are set for bearing-less AC asynchronous motor Count neural network inverse decoupling controller;2) number of patent application CN200510038099.5, it is entitled:Bearing-free switch magnetic-resistance is electronic Machine radial neural network reversed decoupling controller and building method, the invention are radially neural for magnetic suspension switched reluctance motor design Network inverse decoupling controller;3) number of patent application CN200510040065.X, based on nerve network reverse five degrees of freedom without bearing forever Magnetic-synchro electric machine control system and control method, the invention are directed to the controlling party of permanent-magnet synchronous motor with five degrees of freedom without bearing design Method;4) supersonic motor parameter identifications of article numbering 0258-8013 (2004) 07-0117-05 based on RBF neural is pin To the method for supersonic motor parameter identification;Nerve network reverse controller control motor thought is special with this used in three above invention Profit has certain correlation, but neutral net is using RBF neural herein, and the BP networks used with them are not Together;The present invention of article 4 is contrasted in the structure of motor, mathematical model, control method, control difficulty and requirement there are essential distinction, Design to the RBF neural adaptive inverse control of self-bearings motors is provided without related patents and document at present Material.
The content of the invention
The purpose of the present invention is non-linear, the close coupling complication system for self-bearings motors, to suspending power, turns Square power and rotor flux carry out Dynamic Nonlinear Decoupling control using RBF neural Adaptive inverse control device, there is provided a kind of It can make self-bearings motors that there is excellent dynamic and static state performance, and with the change of the resistance parameter of electric machine and anti-loading The strong robustness of disturbance, and every Control performance standard of self-bearings motors can be effectively improved;In addition using RBF god Output such as radial displacement, rotating speed and the magnetic linkage of on-line identification self-bearings motors are realized through network self-adapting identifier.
The technical scheme is that:A kind of self-bearings motors RBF neural adaptive inversion decoupling control and Parameter identification method, including step:
Step 1, it is sent into sensors detection voltage, electric current, tach signal, signal after 3s/2r coordinate transforms Flux Observation Model, to obtain magnetic linkage closed-loop control and the required magnetic linkage information of neural metwork training;
Step 2, SVPWM algoritic modules one and voltage source inverter module one are composed in series to the SVPWM voltage-types of extension Inverter module one, the SVPWM voltage-types that SVPWM algoritic modules two and voltage source inverter module two are composed in series to extension are inverse Become device module two;
Step 3, self-bearings motors and its load module are built, by the SVPWM voltage source inverter modules of extension First, the SVPWM voltage source inverters module two of extension and self-bearings motors and its load module are as an entirety Form composite controlled object;
Step 4, the inverse controller of composite controlled object is built by RBF neural RBFNNC, using offline and online The method being combined is trained and obtains the structure and parameter of RBF neural RBFNNC, by trained RBF neural RBFNNC forms linear control system before being placed in composite controlled object, so as to fulfill the decoupling control to self-bearings motors System;
Step 5, the identifier of composite controlled object is built by RBF neural RBFNNI, utilizes offline and online phase With reference to method training and obtain the structure and parameter of RBF neural RBFNNI, after identification precision reaches design requirement, use Identification signal replaces the signal that sensor detects, and realizes sensorless strategy.
Further, the 3s/2r coordinate transforms in the step 1 can be divided into the first coordinate transform and the second coordinate transform, institute It is the self-bearings motors stator winding phase current i detected by Hall current sensor to state the first coordinate transform1a、 i1b、i1cConvert to obtain electric current i under rotating coordinate system by Clark conversion and Park1d、i1q;Second coordinate transform is by suddenly You detect self-bearings motors stator winding phase voltage U by voltage sensor1a、U1b、U1cBy Clark conversion and Park converts to obtain voltage U under rotating coordinate system1d、U1q
Further, the Flux Observation Model in the step 1 includes stator flux observer model and flux linkage observation mould Type;
The stator flux observer model is by electric current i under rotating coordinate system1d、i1qWith voltage U1d、U1qObtained through functional transformation Stator magnetic linkage component ψ under to rotating coordinate system1d、ψ1q
ψ1d=∫ (U1d-Ri1d)dt-L1i1d
ψ1q=∫ (U1q-Ri1q)dt-L1i1q
The flux linkage observation model is by electric current i under rotating coordinate system1d、i1qWith stator magnetic linkage under rotating coordinate system Component ψ1d、ψ1qRotating coordinate system lower rotor part magnetic linkage component ψ is obtained through functional transformationdr、ψqr
Further, in the step 2, the SVPWM algoritic modules one are by given voltage signal Uα1s*、Uβ1s* be converted to and account for Sky is than signal Sa1s、Sb1s、Sc1s, duty cycle signals are output to voltage source inverter one and produce voltage signal Ua1s、Ub1s、Uc1sCome Control torque winding system;
The SVPWM algoritic modules two are by given voltage signal Uα2s*、Uβ2s* duty cycle signals S is converted toa2s、Sb2s、 Sc2s, duty cycle signals are output to voltage source inverter two and produce voltage signal Ua2s、Ub2s、Uc2sTo control suspending windings system System.
Further, the torque winding system mathematical model of self-bearings motors and its load module in the step 3 For common cage type asynchronous motor mathematical model;The suspending windings system suspension power of self-bearings motors and its load module Math equation is as follows:
Fx=M (- id1sid2s+iq1siq2s)
Fy=M (iq1sid2s+id1siq2s)
M mutual inductances between torque winding and suspending windings in formula;
The suspending windings system state equation equation of self-bearings motors and its load module is as follows:
M is rotor quality in formula;Fsx、FsyIntrinsic Maxwell force, its expression formula are:
Fsx=ksx
Fsy=ksy
In formulaFor radial displacement rigidity;R is rotor radius;L is rotor shaft length;μ0For air magnetic conductance Rate;δ is gas length;K is decay factor, generally takes 0.3.
Further, in the step 4-5, RBF neural RBFNNC and RBF neural RBFNNI structure and parameters are true Fixed off-line training method is:
The number and its center and width of hidden node are determined by off-line training, and is calculated between hidden layer and output layer Connection weight initial value, the input sample of RBFNNC is displacement X, Y, rotational speed omegar, magnetic linkage ψrSet-point and actual value mistake Difference and error signal pass through ecOutput valve { the e of function module1, e2, e3, e4, ec1, ec2, ec3, ec4, output sample is process { the U of coordinate transform, U, U, U};The input sample of RBFNNI outputs and inputs for the composite controlled object after delay {U, U, U, U, X, Y, ωr, ψr, output sample exports { X ', Y ', ω for the composite controlled object of identificationr', ψr′};
Increase number of nodes according to certain rules self-adaptive in the training process, and output signal will be made according to rule Deleted with too small Hidden unit, effectively realize that mission nonlinear maps with minimum Hidden unit.
Further, in the step 4-5, RBF neural RBFNNC and RBF neural RBFNNI structure and parameters are true Least square method of recursion learning rules are used in fixed on-line training method.
The advantage of the invention is that:
1. self-bearings motors had both inherited magnetic bearing supporting motor advantage, solve traditional supports structure and answer It is miscellaneous, volume is big, of high cost, efficiency is low, high failure rate shortcoming, and more reasonable than magnetic axis bearing motor, more practical.1) significantly Axial space is shortened, improves axial utilization rate, the limitation of high-power and ultrahigh rotating speed can be broken through;2) this self-bearings motors Radial suspension control system in power amplification circuit use the three phase power inverter circuit based on SVPWM algorithms so that motor Control low in energy consumption, torque pulsation is small, improves the sine degree of phase current, reduces the THD of electric current.
2. by RBF neural adaptive inversion self-bearings motors are carried out torque force and radial suspension force it Between dynamic decoupling so as to fulfill position system, rotor speed and magnetic linkage control while, this method relative to such as BP nerve Network is inverse the characteristics of its is exclusive:RBF neural has good biological context and Function approximation capabilities, not only structure letter List, fast convergence rate, generalization ability are strong, and have global optimum and Property of Optimal Approximation, further simplify control device network Structure.
3. the characteristics of prominent in the present invention is to use RBF neural Adaptive Identification device, pass through the voltage electricity easily detected Stream signal recognizes radial displacement, rotating speed and the magnetic linkage letter of self-bearings motors in the case of it need not know motor accurate parameters Breath, and identification precision is high, so as to fulfill sensorless strategy, reduces system cost, improves system reliability, especially suitable for Adverse circumstances and system requirements high field are closed.
4. the present invention increases number of nodes according to certain rules self-adaptive in the training process, and will be to defeated according to rule Go out the too small Hidden unit of signal function to delete, to ensure that network structure is simple, compact, effectively realized with minimum Hidden unit Mission nonlinear maps.Had using least square method of recursion study has supervision ground by on-line training by least square method of recursion Regulating networks connection weight, the advantages of strengthening the robustness of inverse system.
Self-bearings motors RBF neural of the present invention based on RBF neural adaptive inversion construction is adaptive Adverse control system, improves self-bearings motors control performance, and is equally applicable to other bearing-free motors control system System and all types of electric machine control systems of magnetic bearing supporting.So the application prospect of this control method is very wide, for it Also there is important application value for his bearing-free motor.
Brief description of the drawings
Fig. 1 is the schematic diagram of self-bearings motors rotor flux observer;
Fig. 2 be by the SVPWM voltage source inverters module one that extends and the SVPWM voltage source inverters module two of extension with And the schematic diagram of self-bearings motors and its load module as an entirety composition composite controlled object;
Fig. 3 is RBF neural schematic internal view and isoboles;
Fig. 4 is that RBF neural inverse network and RBF neural parameter identification network collectively form compound input and output Practise sample schematic diagram;
Fig. 5 is self-bearings motors RBF neural adaptive inversion decoupling and controlling system block diagram;
Fig. 6 is self-bearings motors RBF neural Adaptive inverse control and parameter identification system block diagram.
Embodiment
Embodiments of the present invention are:Become first using common electric current, voltage, speed, Flux Observation Model and Park A flux observer with Clark conversion compositions is changed, to estimate the rotor of the self-bearings motors needed for magnetic linkage closed-loop Magnetic linkage information.Two SVPWM and voltage source inverter module, and self-bearings motors and its load module one are acted as Composite controlled object is formed for an entirety, the controlled volume of composite controlled object is self-bearings motors rotor radial position Shifting, rotating speed and magnetic linkage;Build the inverse controller of composite controlled object using RBF neural, inverse controller input be to The error signal for determining signal and feedback signal forms closed loop;In addition realized using a RBF neural RBFNNI to controlled pair Rotating speed and the displacement signal identification of elephant;Two RBF neurals all use three layers of feed forward type network, including input layer (8 sections Point), hidden layer and output layer (4 nodes), wherein hidden layer uses radial basis function, the side being combined by offline and on-line study Formula realizes network structure initialization and right-value optimization;Finally recognize RBF neural adaptive inversion system and auto-adaptive parameter, It is adaptive that two SVPWM and voltage source inverter module collectively form RBF neural adaptive inversion self-bearings motors Adverse control system realizes the independent control to self-bearings motors torque force and radial suspension force, so as to fulfill motor dynamics Decoupling and object parameters identification.
The embodiment of the present invention is further illustrated below in conjunction with the accompanying drawings.
As shown in Figure 1, construction self-bearings motors flux observer:By two coordinate transforms 11,12, stator magnetic linkage Observation model 13 and rotor flux identification model 14 form;One of coordinate transform is by self-bearings motors stator Winding phase current i1a、i1b、i1cThe winding phase current i of self-bearings motors is gathered by 3s/2r conversion 111d、i1q;Separately It is self-bearings motors stator winding phase voltage U that one 3s/2r, which converts 12 coordinate transforms,1a、U1b、U1cBecome by 3s/2r 12 are changed to gather the winding phase voltage U of self-bearings motors1d、U1q;Then module is recognized by corresponding magnetic linkage to obtain Required magnetic linkage value.The stator flux observer model 13 is by electric current i under rotating coordinate system1d、i1qWith voltage U1d、U1qThrough letter Transformation of variables obtains stator magnetic linkage component ψ under rotating coordinate system1d、ψ1q
ψ1d=∫ (U1d-Ri1d)dt-L1i1d
ψ1q=∫ (U1q-Ri1q)dt-L1i1q
The flux linkage observation model 14 is by electric current i under rotating coordinate system1d、i1qWith stator magnet under rotating coordinate system Chain component ψ1d、ψ1qRotating coordinate system lower rotor part magnetic linkage component ψ is obtained through functional transformationdr、ψqr
In Fig. 2, SVPWM algoritic modules 1 and SVPWM algoritic modules 2 31 are common SVPWM algorithms;Voltage-type inversion Device 1 and voltage source inverter 2 32 are voltage-type power inverter IPM;Given voltage signal Uα1s*、Uβ1s* calculated through SVPWM Method module 1 obtains duty cycle signals Sa1s、Sb1s、Sc1s, duty cycle signals be output to voltage source inverter 1 produce electricity Press signal Ua1s、Ub1s、Uc1sTo control torque system;Given voltage signal Uα2s*、Uβ2s* obtained through SVPWM algoritic modules 2 31 Duty cycle signals Sa2s、Sb2s、Sc2s, duty cycle signals are output to voltage source inverter 2 32 and produce voltage signal Ua2s、Ub2s、 Uc2sTo control suspension system.
As shown in Figure 2 by two SVPWM21,31 and voltage source inverter module 22,32, and self-bearings motors And its load module 1 forms composite controlled object 4 together as an entirety, the controlled volume input of composite controlled object is yes {U, U, U, UVoltage signal, the displacement of self-bearings motors rotor radial, rotating speed and magnetic linkage are as output.
The core design of the present invention is the design and learning method of RBF neural.Initially set up RBF as shown in Figure 3 Neutral net RBFNN 75, as seen from the figure input layer 71 have 8 nodes, hidden layer 72 is the node that radial basis function is formed, radially Basic function uses gaussian kernel function, and the output of i-th of Hidden unit is:
H in formulaiFor the output of i-th of hidden node;X is input vector;CiFor the center of i-th of hidden node;biFor this Hidden layer width;| | * | | it is euclideam norm;4 output nodes 74 are obtained by add operation;Established according to above method RBF neural RBFNNC 51 and RBF neural RBFNNI 52.
RBF neural is combined by off-line learning and on-line study and is trained, and training sample is as shown in Figure 4: The input sample of RBFNNC 51 is displacement X, Y, rotational speed omegar, magnetic linkage ψrSet-point and value of feedback error signal and error signal Through ecFunction:
eci(t)=ei(t)-ei(t-1) (i=1,2,3,4)
Output valve { e1, e2, e3, e4, ec1, ec2, ec3, ec4, output sample is the { U by coordinate transform, U, U, U};The input sample of RBFNNI52 outputs and inputs { U for the composite controlled object of delay, U, U, U, X, Y, ωr, ψr, output sample exports { X ', Y ', ω for the composite controlled object of identificationr', ψr′}.Off-line training is first carried out, by RBF nerves The Hidden unit initial value of network is set as zero, and hidden node is adaptively added according to " novelty " condition, is deleted using one kind Strategy, as study constantly reduces to a certain extent sluggish knot removal into being about to those contributions to output, with true It is simple, compact to protect network structure, the Nonlinear Mapping of system is effectively realized with minimum Hidden unit, off-line training determines network Structure simultaneously initializes hidden layer center and output weights;By on-line study, pass through gradient descent method on-line amending Network parameters, make network-adaptive environmental change.
It is described effectively to realize that the specific algorithm that mission nonlinear maps is as follows with minimum Hidden unit:
For i-th of learning sample (x (i), Z (i)),
Step 7.1, c is initializedjFor any real number, each Hidden unit output of RBF neural is calculated respectivelyWith Network output y (i):
Step 7.2, calculation error | | E (i) | |=| | Z (i)-y (i) | |, Z (i) exports for target in formula, i.e., system is through adopting Record after sample conditioning, y (i) is network reality output, and calculates the distance of sample and already present hidden layer:
dj=| | x (i)-cj| |, j=1,2 ..., m
M is already present Hidden unit number in formula;
Make dmin=min (dj)
Step 7.3, if | | Ei| | > ε, dmin> λ (i), then:
λ (i)=max (λmaxγi, λmin)
ε is the desired precision of network in formula;The fitting precision of network when λ (i) is i-th of input, with the carry out λ of study (i) from λmaxIt is reduced to λmin;γ is decay factor, and 0 < γ < 1, then increase a Hidden unit, its parameter:
ck=xi
C in formulajFor the center of the p Hidden unit nearest from input sample, p=2 is taken here;
Step 7.4, otherwise, just least square method of recursion regulating networks connection weight is pressed;
Step 7.5, if all meeting for contact n sample of input:
σ is predefined constant in formula, if formula conditional when i=1,2 meets at the same time, when j-th hidden node is deleted;
Step 7.6, i+1 group sample is inputted, is repeated the above process.
In the step 4-5, RBF neural RBFNNC51 and RBF neural RBFNNI52 structure and parameters determine On-line training method use least square method of recursion learning rules:
Step 8.1, kth group is inputted, re-establishes network output equation:
ω (k), u (k) are respectively weighted vector and radial basis function vector in formula, and H represents conjugate transposition;
Step 8.2, P (0)=δ is made-1I, ω (0)=0;
Step 8.3, calculate
ζ (k)=y (k)-ωH(k-1)u(k)
ω (k)=ω (k-1)+v (k) ζ*(k)
P (k)=η-1P(k-1)-η-1v(k)uH(k)P(k-1)
δ is on the occasion of small constant in formula;η is forgetting factor, 0≤η≤1;* complex conjugate is represented.
As shown in Figure 5 by the RBF neural established against 50 and two extend SVPWM voltage source inverters 2,3 knots Close and form RBF neural Adaptive inverse control device 6, be placed on before self-bearings motors, realize decoupling control.
As Fig. 6 forms control system:By RBF neural RBFNNC51 and RBF neural RBFNNI52, two SVPWM 21,31 and voltage source inverter module 22,32 collectively form RBF neural adaptive inversion self-bearings motors Adaptive inverse control and parameter identification system 7.
The present invention can be realized according to above-mentioned attached drawing and its correlation step.
The technical principle of the present invention is summarized further below.
The principle of the present invention is to change traditional self-bearings motors to use rotor field-oriented and Air-gap-flux orientated The strategy of decoupling control, design invention is a kind of to use RBF neural adaptive inverse control to self-bearings motors Carry out Dynamic Nonlinear Decoupling control.
The adaptive Inverted control system of RBF neural and parameter identification method of the present invention is adaptive using RBF neural Answer inverse controller to replace the team in existing decoupling control method to answer inverse system model, compensate for such as Air-gap-flux orientated, rotor The dynamic decoupling of torque force and suspending power has been better achieved in the deficiency of field orientation and BP neural network reversed decoupling, this method, On-line identification precision is high, and reducing sensor use reduces cost, strengthens governing system reliability, while make the asynchronous electricity of bearing-free Motivation governing system has stronger anti-interference and robustness.
The RBF neural Adaptive inverse control of self-bearings motors and the control method of parameter identification system are: First using common electric current, voltage, speed, Flux Observation Model and Park conversion and a magnetic linkage of Clark conversion compositions Observer, to estimate the rotor flux information of the self-bearings motors needed for magnetic linkage closed-loop.By two SVPWM and voltage-type Inverter module, and self-bearings motors and its load module form composite controlled object together as an entirety, The controlled volume of composite controlled object is the displacement of self-bearings motors rotor radial, rotating speed and magnetic linkage;Using a RBF nerve The inverse system of network struction composite controlled object, inverse system input are formed for the error signal of Setting signal and feedback signal and closed Ring, realizes decoupling control between torque force and radial suspension force;In addition realized using a RBF neural without speed and without position Displacement sensor controls, and realizes and self-bearings motors Dynamic Nonlinear Decoupling is controlled;It is finally that RBF neural is adaptive Inverse controller and auto-adaptive parameter identifier, that two SVPWM and voltage source inverter module collectively forms RBF neural is adaptive It should be realized against self-bearings motors adaptive inverse control to self-bearings motors torque force and radial suspension force Independent control, so as to fulfill rotor stable suspersion and operation.
Wherein above-mentioned flux observer is recognized by two coordinate transforms, stator flux observer model and rotor flux Model forms;One of coordinate transform is by self-bearings motors stator winding phase current i1a、i1b、i1cPass through Clark Conversion and Park become the winding phase current i for bringing collection self-bearings motors1d、i1q;Another coordinate transform is nothing Bearing asynchronous motor stator winding phase voltage U1a、U1b、U1cIt is different that collection bearing-free is brought by Clark conversion and Park changes Walk the winding phase voltage U of motor1d、U1q;Then module is recognized by corresponding magnetic linkage to obtain required magnetic linkage value.
It should be understood that above-mentioned example of applying is only illustrative of the invention and is not intended to limit the scope of the invention, the present invention is being read Afterwards, modification of the those skilled in the art to the various equivalent forms of the present invention falls within the application appended claims and is limited Scope.

Claims (7)

1. self-bearings motors RBF neural adaptive inversion decoupling control and parameter identification method, it is characterised in that bag Include step:
Step 1, with sensors detection voltage, electric current, tach signal, signal magnetic linkage is sent into after 3s/2r coordinate transforms Observation model, to obtain magnetic linkage closed-loop control and the required magnetic linkage information of neural metwork training;
Step 2, SVPWM algoritic modules one (21) and voltage source inverter module one (22) are composed in series to the SVPWM electricity of extension Die mould inverter module one (2), extension is composed in series by SVPWM algoritic modules two (31) and voltage source inverter module two (32) SVPWM voltage source inverters module two (3);
Step 3, self-bearings motors and its load module (1) are built, by the SVPWM voltage source inverters module one of extension (2), the SVPWM voltage source inverters module two (3) of extension and self-bearings motors and its load module (1) are used as one A entirety composition composite controlled object (4);
Step 4, the inverse controller of composite controlled object (4) is built by RBF neural RBFNNC (51), using it is offline and The method that line is combined is trained and obtains the structure and parameter of RBF neural RBFNNC (51), by trained RBF nerve nets Network RBFNNC (51) forms linear control system before being placed in composite controlled object (4), so as to fulfill to bearing-free asynchronous electric The decoupling control of machine;
Step 5, the identifier of composite controlled object (4) is built by RBF neural RBFNNI (52), using offline and online The method being combined is trained and obtains the structure and parameter of RBF neural RBFNNI (52), and reaching design in identification precision will After asking, the signal that sensor detects is replaced with identification signal, realizes sensorless strategy.
2. self-bearings motors RBF neural adaptive inversion decoupling control according to claim 1 and parameter are distinguished Knowledge method, it is characterised in that the 3s/2r coordinate transforms in the step 1 can be divided into the first coordinate transform (11) and the second coordinate Convert (12), first coordinate transform (11) is the self-bearings motors stator detected by Hall current sensor Winding phase current i1a、i1b、i1cConvert to obtain electric current i under rotating coordinate system by Clark conversion and Park1d、i1q;Second sits Mark conversion (12) is that handle detects self-bearings motors stator winding phase voltage U by Hall voltage sensor1a、U1b、U1cIt is logical Cross Clark conversion and Park converts to obtain voltage U under rotating coordinate system1d、U1q
3. self-bearings motors RBF neural adaptive inversion decoupling control according to claim 2 and parameter are distinguished Knowledge method, it is characterised in that the Flux Observation Model in the step 1 includes stator flux observer model (13) and rotor flux Observation model (14);
The stator flux observer model (13) is by electric current i under rotating coordinate system1d、i1qWith voltage U1d、U1qObtained through functional transformation Stator magnetic linkage component ψ under to rotating coordinate system1d、ψ1q
ψ1d=∫ (U1d-Ri1d)dt-L1i1d
ψ1q=∫ (U1q-Ri1q)dt-L1i1q
The flux linkage observation model (14) is by electric current i under rotating coordinate system1d、i1qWith stator magnetic linkage under rotating coordinate system point Measure ψ1d、ψ1qRotating coordinate system lower rotor part magnetic linkage component ψ is obtained through functional transformationdr、ψqr
<mrow> <msub> <mi>&amp;psi;</mi> <mrow> <mi>d</mi> <mi>r</mi> </mrow> </msub> <mo>=</mo> <msub> <mi>i</mi> <mrow> <mn>1</mn> <mi>d</mi> </mrow> </msub> <msub> <mi>L</mi> <mrow> <mi>m</mi> <mn>1</mn> <mi>r</mi> </mrow> </msub> <mo>-</mo> <msub> <mi>T</mi> <mi>r</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>&amp;omega;</mi> <mi>r</mi> </msub> <msub> <mi>&amp;psi;</mi> <mrow> <mn>1</mn> <mi>d</mi> </mrow> </msub> <mo>+</mo> <mfrac> <mrow> <msub> <mi>d&amp;psi;</mi> <mrow> <mi>d</mi> <mi>r</mi> </mrow> </msub> </mrow> <mrow> <mi>d</mi> <mi>t</mi> </mrow> </mfrac> <mo>)</mo> </mrow> </mrow>
<mrow> <msub> <mi>&amp;psi;</mi> <mrow> <mi>q</mi> <mi>r</mi> </mrow> </msub> <mo>=</mo> <msub> <mi>i</mi> <mrow> <mn>1</mn> <mi>q</mi> </mrow> </msub> <msub> <mi>L</mi> <mrow> <mi>m</mi> <mn>1</mn> <mi>r</mi> </mrow> </msub> <mo>-</mo> <msub> <mi>T</mi> <mi>r</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>&amp;omega;</mi> <mi>r</mi> </msub> <msub> <mi>&amp;psi;</mi> <mrow> <mn>1</mn> <mi>q</mi> </mrow> </msub> <mo>+</mo> <mfrac> <mrow> <msub> <mi>d&amp;psi;</mi> <mrow> <mi>q</mi> <mi>r</mi> </mrow> </msub> </mrow> <mrow> <mi>d</mi> <mi>t</mi> </mrow> </mfrac> <mo>)</mo> </mrow> <mo>.</mo> </mrow>
4. self-bearings motors RBF neural adaptive inversion decoupling control according to claim 1 and parameter are distinguished Knowledge method, it is characterised in that in the step 2,
The SVPWM algoritic modules one (21) are by given voltage signal Uα1s*、Uβ1s* duty cycle signals S is converted toa1s、Sb1s、 Sc1s, duty cycle signals are output to voltage source inverter module one (22) and produce voltage signal Ua1s、Ub1s、Uc1sTo control torque Winding system;
The SVPWM algoritic modules two (31) are by given voltage signal Uα2s*、Uβ2s* duty cycle signals S is converted toa2s、Sb2s、 Sc2s, duty cycle signals are output to voltage source inverter module two (32) and produce voltage signal Ua2s、Ub2s、Uc2sSuspend to control Winding system.
5. self-bearings motors RBF neural adaptive inversion decoupling control according to claim 1 and parameter are distinguished Knowledge method, it is characterised in that the torque winding system number of self-bearings motors and its load module (1) in the step 3 Model is common cage type asynchronous motor mathematical model;The suspending windings of self-bearings motors and its load module (1) System suspension power math equation is as follows:
Fx=M (- id1sid2s+iq1siq2s)
Fy=M (iq1sid2s+id1siq2s)
M mutual inductances between torque winding and suspending windings in formula;
The suspending windings system state equation equation of self-bearings motors and its load module (1) is as follows:
<mrow> <mi>m</mi> <mover> <mi>x</mi> <mo>&amp;CenterDot;&amp;CenterDot;</mo> </mover> <mo>=</mo> <msub> <mi>F</mi> <mi>x</mi> </msub> <mo>-</mo> <msub> <mi>F</mi> <mrow> <mi>s</mi> <mi>x</mi> </mrow> </msub> </mrow>
<mrow> <mi>m</mi> <mover> <mi>y</mi> <mo>&amp;CenterDot;&amp;CenterDot;</mo> </mover> <mo>=</mo> <msub> <mi>F</mi> <mi>y</mi> </msub> <mo>-</mo> <msub> <mi>F</mi> <mrow> <mi>s</mi> <mi>y</mi> </mrow> </msub> </mrow>
M is rotor quality in formula;Fsx、FsyIntrinsic Maxwell force, its expression formula are:
Fsx=ksx
Fsy=ksy
In formulaFor radial displacement rigidity;R is rotor radius;L is rotor shaft length;μ0For air permeability;δ is Gas length;K is decay factor, generally takes 0.3.
6. self-bearings motors RBF neural adaptive inversion decoupling control according to claim 1 and parameter are distinguished Knowledge method, it is characterised in that in the step 4-5, RBF neural RBFNNC (51) and RBF neural RBFNNI (52) The off-line training method that structure and parameter determines is:
The number and its center and width of hidden node are determined by off-line training, and calculates the company between hidden layer and output layer Connect the initial value of power, the input sample of RBFNNC (51) is displacement X, Y, rotational speed omegar, magnetic linkage ψrSet-point and actual value mistake Difference and error signal pass through ecOutput valve { the e of function module1,e2,e3,e4,ec1,ec2,ec3,ec4, output sample is process { the U of coordinate transform,U,U,U};The input sample of RBFNNI (52) for composite controlled object after delay input and Export { U,U,U,U,X,Y,ωrr, output sample exports { X', Y', ω for the composite controlled object of identificationr', ψr'};
Increase number of nodes according to certain rules self-adaptive in the training process, and will be to exporting signal function mistake according to rule Small Hidden unit is deleted, and effectively realizes that mission nonlinear maps with minimum Hidden unit.
7. self-bearings motors RBF neural adaptive inversion decoupling control according to claim 1 and parameter are distinguished Knowledge method, it is characterised in that in the step 4-5, RBF neural RBFNNC (51) and RBF neural RBFNNI (52) Least square method of recursion learning rules are used in the on-line training method that structure and parameter determines.
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