CN106130431A - A kind of linear electric motors RBF neural generalized inverse internal model control method - Google Patents
A kind of linear electric motors RBF neural generalized inverse internal model control method Download PDFInfo
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- CN106130431A CN106130431A CN201610577364.5A CN201610577364A CN106130431A CN 106130431 A CN106130431 A CN 106130431A CN 201610577364 A CN201610577364 A CN 201610577364A CN 106130431 A CN106130431 A CN 106130431A
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- 238000013178 mathematical model Methods 0.000 abstract description 4
- 230000004044 response Effects 0.000 abstract description 2
- 238000013528 artificial neural network Methods 0.000 description 4
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Classifications
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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
- H02P25/06—Linear motors
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
- H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
- H02P21/0003—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control
- H02P21/0014—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control using neural networks
Abstract
The invention discloses a kind of linear electric motors RBF neural generalized inverse internal model control method, by being approached the permanent-magnetism linear motor Generalized Inverse System without mathematical models by RBF neural, and it is concatenated with permanent-magnetism linear motor system, thus realize linearisation and the decoupling of permanent-magnetism linear motor control system, the correction by internal model control of the pseudo-linear system after decoupling, makes the key performances such as permanent-magnetism linear motor control system control accuracy, dynamic response capability, robustness more superior.
Description
Technical field
The present invention relates to a kind of linear electric motors RBF neural generalized inverse internal model control method, belong to Electric Drive technology
Field.
Background technology
Electric energy can be converted into the mechanical energy of linear motion by linear electric motors, not only eliminates intermediate transmission mechanism, and
And reducing system loss, it is high that permanent-magnet linear motor has power density, and therefore the features such as controllability is good pass at current electric power
Move and servo-control system applied increasingly extensive.
Permanent-magnetism linear motor there is also simultaneously and changes the features such as parameter sudden change, non-linear close coupling, this control with running status
System processed has multiple-input and multiple-output and cannot obtain mathematical model accurately, and therefore Traditional control theory cannot meet the modern times
The demand for control of high-performance linear motor.
Neural network generalized inverse system has taken into account the feature of the Linearized Decoupling of inverse system and neutral net to nonlinear system
System approximation capability；The modeling error needs that the pseudo-linear system being made up of Neural Network Inverse System concatenation permanent-magnetism linear motor exists
Introducing closed loop controller to eliminate, accessing of internal mode controller will ensure the realization by ensureing this performance；Therefore, neutral net
Generalized inverse internal model control system controls have stronger adaptive ability and robustness to the system of non-linear close coupling.
Summary of the invention:
For features such as permanent-magnet linear motor poor anti jamming capability, the present invention provides a kind of linear electric motors RBF neural wide
The inverse internal model control method of justice, can effectively disturbance cancelling signal and the uncertain environment impact on control system, raising permanent-magnet linear
Motor dynamics responding ability and robustness.
For achieving the above object, the present invention provides following technical scheme:
A kind of linear electric motors RBF neural generalized inverse internal model control method, including: Generalized Inverse System, linear induction motor system, PI
Actuator, PD actuator, internal mode controller, coordinate transformation module；Wherein Generalized Inverse System includes: RBF neural generalized inverse
Module, electric current loop First-order Integral module, der Geschwindigkeitkreis Second Order Integral module；Linear induction motor system includes: SVPWM modulation module, inverse
Become device, linear electric motors module, specifically comprise the following steps that
Step one: linear electric motors outfan is measured by optical rotary encoder with for detecting the transformer of phase current and calculated
Obtain rotational speed omegar, electrical angle θ, phase current ia、ib、ic 。
Step 2: phase current and electrical angle be two rotational coordinates electric current i of gained after changes in coordinatesd、iq, wherein idDefeated
Go out and be connected with RBF neural generalized inverse module, iqWith input current signal iq *It is connected；Linear electric motors output speed ωr
With input speed signal ωr *It is connected；Linear electric motors output electrical angle θ is connected with SVPWM modulation module.
Step 3: by feedback current iqAnd rotational speed omegarWith input current signal iq *And tach signal ωr *Done deviation is fed back
To internal mode controller, after internal mode controller correction, it is separately input to pi regulator and PD actuator is adjusted.Pi regulator
And PD actuator output is connected with First-order Integral device and second-order integrator respectively.
Step 4: the output of electric current First-order Integral device, rotating speed second-order integrator and the i through coordinate transform gaineddWith RBF god
It is connected through network generalized inverse module input, thus constitutes linear electric motors Generalized Inverse System；RBF neural generalized inverse module
Output ud、uqAnd feedback end electrical angle θ is respectively connected to SVPWM modulation module, thus it is wide to constitute linear electric motors RBF neural
The inverse internal model control model of justice.
The beneficial effects of the present invention is, compared with prior art, the present invention is neural by a kind of linear electric motors RBF of design
Network generalized inverse internal model control method, by being approached the permanent-magnet linear electricity without mathematical models by RBF neural
Machine Generalized Inverse System, and it is concatenated with permanent-magnetism linear motor system, thus realize the linear of permanent-magnetism linear motor control system
Change and decoupling, the correction by internal model control of the pseudo-linear system after decoupling, make permanent-magnetism linear motor control system control essence
The key performances such as degree, dynamic response capability, robustness are more superior.
Accompanying drawing explanation
Fig. 1 is the linear electric motors RBF neural generalized inverse internal model control method structured flowchart of the embodiment of the present invention.
Fig. 2 is that RBF neural generalized inverse decouples as pseudo-linear system schematic diagram.
Detailed description of the invention
In conjunction with accompanying drawing, present example is elaborated.
As it is shown in figure 1, linear electric motors RBF neural generalized inverse internal model control method, including: Generalized Inverse System, straight line
Electric system, pi regulator, PD actuator, internal mode controller, coordinate transformation module.Wherein Generalized Inverse System includes: RBF god
Through network generalized inverse module, electric current loop First-order Integral module, der Geschwindigkeitkreis Second Order Integral module；Linear induction motor system includes: SVPWM
Modulation module, inverter, linear electric motors module.
Linear electric motors outfan is measured by optical rotary encoder with for detecting the transformer of phase current and is calculated
To rotational speed omegar, electrical angle θ, phase current ia、ib、ic .Phase current and electrical angle be two rotational coordinates of gained after changes in coordinates
Electric current id、iq, wherein idOutput is connected with RBF neural generalized inverse module, iqWith input current signal iq *It is connected；Directly
Line motor output speeds ωrWith input speed signal ωr *It is connected；Linear electric motors output electrical angle θ and SVPWM modulation module phase
Connect.
By feedback current iqAnd rotational speed omegarWith input current signal iq *And tach signal ωr *Done deviation feeds back to interior mould
Controller, is separately input to pi regulator after internal mode controller correction and PD actuator is adjusted.Pi regulator and PD adjust
Joint device output is connected with First-order Integral device and second-order integrator respectively.
Electric current First-order Integral device, rotating speed second-order integrator output and through the i of coordinate transformation module gaineddNeural with RBF
Network generalized inverse module input is connected, thus constitutes linear electric motors Generalized Inverse System；RBF neural generalized inverse module is defeated
Output ud、uqAnd feedback end electrical angle θ is respectively connected to SVPWM modulation module, thus the decoupling of completion system and linearisation such as Fig. 2
Shown in.
The pseudo-linear system access internal mode controller completing decoupling is modeled the adjustment of parameter, and then constitutes linear electric motors
RBF neural generalized inverse internal model control model.
In the implementation case, employing MATLAB/Simulink with dSPACE combines as a example by driving permanent-magnetism linear motor,
The enforcement of the control method of the detailed description present invention:
Step 1: the structure to RBF neural generalized inverse module carries out data sampling, the most really in MATLAB/Simulink
Determine sampled signal, [the i when neural metwork trainingd,id´,ωr,ωr´,ωr] composition neutral net input, [ud, uq] group
Become the output of neutral net, so having only to gather idAnd ωrAs sample.
Step 2: after enough training datas of sampling, determines the number of plies and the nodes of selected RBF neural, at RBF
Add normalization and renormalization module before and after neural network module, and training iterations is set and selects suitably to train letter
Number, Training RBF Neural Network, between being exported by reality and expected, error selects Approximation effect.
Step 3: select the RBF neural generalized inverse module that Approximation effect is good, in the phantom built, will
RBF neural generalized inverse module input is connected with First-order Integral subsystem, Second Order Integral subsystem thus constitutes broad sense
Inverse system.
Step 4: build SVPWM modulation module, inverter module, linear electric motors module in phantom.
Step 5: exported electrical angle θ, phase current i by linear electric motors modulea、ib、ic It is connected with coordinate transformation module, sits
Mark conversion module output idIt is connected with RBF neural generalized inverse module, iqWith input current signal iq *It is connected；Straight-line electric
Machine module output speed ωrWith input speed signal ωr *It is connected；Linear electric motors module output electrical angle θ modulates mould with SVPWM
Block is connected.
Step 6: design internal mode controller inner parameter, by feedback current iqAnd rotational speed omegarWith input current signal iq *And
Tach signal ωr *Done deviation feeds back to internal mode controller, is separately input to pi regulator and PD after internal mode controller correction
Actuator is adjusted.
Step 7: combined by the RTI of RTW with dSPACE by MATLAB/Simulink, by entering each module and I/O
Phantom is converted into digital drive signals by line parameter configuration, and the mathematical model in former analogue system is converted to have reality
The controlled device of physical significance, designs Driven by inverter linear electric motors, and observed and recorded experimental waveform adjusts RBF neural in good time
Generalized inverse linear motor control system variable parameter.
Claims (1)
1. a linear electric motors RBF neural generalized inverse internal model control method, it is characterised in that: including: Generalized Inverse System, straight
Line electric system, pi regulator, PD actuator, internal mode controller, coordinate transformation module；Wherein Generalized Inverse System includes: RBF god
Through network generalized inverse module, electric current loop First-order Integral module, der Geschwindigkeitkreis Second Order Integral module；Linear induction motor system includes: SVPWM
Modulation module, inverter, linear electric motors module, specifically comprise the following steps that
Step one: linear electric motors outfan is measured by optical rotary encoder with for detecting the transformer of phase current and calculated
Obtain rotational speed omegar, electrical angle θ, phase current ia、ib、ic ；
Step 2: phase current and electrical angle be two rotational coordinates electric current i of gained after changes in coordinatesd、iq, wherein idOutput with
RBF neural generalized inverse module is connected, iqWith input current signal iq *It is connected；Linear electric motors output speed ωrWith defeated
Enter tach signal ωr *It is connected；Linear electric motors output electrical angle θ is connected with SVPWM modulation module；
Step 3: by feedback current iqAnd rotational speed omegarWith input current signal iq *And tach signal ωr *In done deviation feeds back to
Mould controller, is separately input to pi regulator after internal mode controller correction and PD actuator is adjusted；
Step 4: pi regulator and PD actuator output are connected with First-order Integral device and second-order integrator respectively；
Step 5: the output of electric current First-order Integral device, rotating speed second-order integrator and the i through coordinate transform gaineddWith RBF nerve net
Network generalized inverse module input is connected, thus constitutes linear electric motors Generalized Inverse System；RBF neural generalized inverse module exports
Amount ud、uqAnd feedback end electrical angle θ is respectively connected to SVPWM modulation module, thus constitute linear electric motors RBF neural generalized inverse
Internal model control model.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108365787A (en) * | 2018-03-23 | 2018-08-03 | 东南大学 | A kind of Permanent-magnet Synchronous-motor Speed Servo System and its design method based on internal model control |
| CN110244563A (en) * | 2019-06-18 | 2019-09-17 | 华北电力大学 | A kind of identification of neural Networks Internal Model Control device model mismatch and online updating method |
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| CN101917150A (en) * | 2010-06-24 | 2010-12-15 | 江苏大学 | Robust controller of permanent magnet synchronous motor based on fuzzy-neural network generalized inverse and construction method thereof |
| JP2011030393A (en) * | 2009-07-29 | 2011-02-10 | Fuji Electric Systems Co Ltd | Controller of linear permanent magnet synchronous motor |
| CN102790581A (en) * | 2012-08-06 | 2012-11-21 | 江苏大学 | Constructing method for robust controller for radial position of bearingless asynchronous motor |
| CN104022701A (en) * | 2014-06-20 | 2014-09-03 | 福州大学 | Method for controlling internal model speed of permanent magnet synchronous linear motor through Newton method |
| CN105790661A (en) * | 2016-04-22 | 2016-07-20 | 江苏大学 | Linear permanent magnet vernier motor decoupling control method based on improved regression support vector machine generalized inverse |
-
2016
- 2016-07-21 CN CN201610577364.5A patent/CN106130431B/en active Active
Patent Citations (6)
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|---|---|---|---|---|
| US20060043921A1 (en) * | 2004-08-30 | 2006-03-02 | Hirokazu Nagura | Control apparatus and method for linear synchronous motor |
| JP2011030393A (en) * | 2009-07-29 | 2011-02-10 | Fuji Electric Systems Co Ltd | Controller of linear permanent magnet synchronous motor |
| CN101917150A (en) * | 2010-06-24 | 2010-12-15 | 江苏大学 | Robust controller of permanent magnet synchronous motor based on fuzzy-neural network generalized inverse and construction method thereof |
| CN102790581A (en) * | 2012-08-06 | 2012-11-21 | 江苏大学 | Constructing method for robust controller for radial position of bearingless asynchronous motor |
| CN104022701A (en) * | 2014-06-20 | 2014-09-03 | 福州大学 | Method for controlling internal model speed of permanent magnet synchronous linear motor through Newton method |
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Cited By (3)
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| CN108365787A (en) * | 2018-03-23 | 2018-08-03 | 东南大学 | A kind of Permanent-magnet Synchronous-motor Speed Servo System and its design method based on internal model control |
| CN110244563A (en) * | 2019-06-18 | 2019-09-17 | 华北电力大学 | A kind of identification of neural Networks Internal Model Control device model mismatch and online updating method |
| CN110244563B (en) * | 2019-06-18 | 2020-10-27 | 华北电力大学 | Neural network internal model controller model mismatch identification and online updating method |
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Effective date of registration: 20231022 Address after: 230000 B-2704, wo Yuan Garden, 81 Ganquan Road, Shushan District, Hefei, Anhui. Patentee after: HEFEI LONGZHI ELECTROMECHANICAL TECHNOLOGY Co.,Ltd. Address before: 230000 floor 1, building 2, phase I, e-commerce Park, Jinggang Road, Shushan Economic Development Zone, Hefei City, Anhui Province Patentee before: Dragon totem Technology (Hefei) Co.,Ltd. Effective date of registration: 20231022 Address after: 230000 floor 1, building 2, phase I, e-commerce Park, Jinggang Road, Shushan Economic Development Zone, Hefei City, Anhui Province Patentee after: Dragon totem Technology (Hefei) Co.,Ltd. Address before: 223800 South Huanghe Road, Suqian City, Jiangsu Province, 399 Patentee before: SUQIAN College |
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