CN1655438A - Magnetic levitation switch reluctance motor radial neural network reversed decoupling controller and method for constructing same - Google Patents
Magnetic levitation switch reluctance motor radial neural network reversed decoupling controller and method for constructing same Download PDFInfo
- Publication number
- CN1655438A CN1655438A CNA2005100380995A CN200510038099A CN1655438A CN 1655438 A CN1655438 A CN 1655438A CN A2005100380995 A CNA2005100380995 A CN A2005100380995A CN 200510038099 A CN200510038099 A CN 200510038099A CN 1655438 A CN1655438 A CN 1655438A
- Authority
- CN
- China
- Prior art keywords
- neural network
- input
- reluctance motor
- controller
- radial position
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Landscapes
- Control Of Electric Motors In General (AREA)
Abstract
This invention relates to a simultaneous control applied in magnetic suspension switch magnetic resistance motor, which comprises the following steps: adopting extensive flow control inverter, wherein the controlled magnetic suspension switch magnetism motor forms compound controlled subject; forming neural net athwart by static neural net and integral device through formula study; connecting neutral net with the compound controlled subject then compounding into two radius direction position sub system to forming anti-linear system; making the linear loop controller to the false linear system according to the linear system design; finally connecting the neural net with the linear loop controller and with the flow controlled inverter to form neural net decoupling controller.
Description
Technical field
The present invention is a kind of reluctance motor with magnistor radial neural network reversed decoupling controller and building method, is applicable to the high performance control of reluctance motor with magnistor, belongs to the technical field of Electric Drive control appliance.
Background technology
At a high speed, the ultrahigh speed motor outstanding problem is the mechanical bearing wear problem, mechanical friction has not only increased the frictional resistance of rotor, and can cause the parts heating, can cause motor gas-gap inhomogeneous when serious, the dynamic characteristic variation that causes motor, thereby the reduction efficiency of motor, the useful life of shortening motor and equipment.Reluctance motor with magnistor (also claim the bearing-free switch magnetic-resistance motor, be called for short BSRM) is not need to design special mechanical bearing, realizes the stable suspersion of motor rotor with magnetic levitation technology.It is laminated to the winding that produces suspending power on the stator of switched reluctance motor, explores the decoupling method that produces radial suspension force by research, realizes the stable suspersion of the rotor of independent control reluctance motor with magnistor.
The employing method is the inverted structure of structure employing of reluctance motor with magnistor at present, because inverted structure can not considered the influence of rotor weight, thereby control is simplified.Adopt feedforward compensation to improve radial displacement, overcome, unbalance vibration when causing the rotor running because the machining of rotor own causes rotor eccentricity.Realize moving under empty load of motor and the loading condition by the suitable leading phase angle of square wave current and the pulsewidth of electric current in the control stator winding.Switched reluctance motor is to move by the variation of controlling the magnetic resistance size, and along with the variation of rotor-position, the changes of magnetic field under certain magnetic potential is very big.And parameter variation and disturbing influence are very greatly, realize the stable suspersion and the controlled rotation of reluctance motor with magnistor, must at first carry out dynamic decoupling to the suspending power under the diverse location, seek the control law of its diverse location.
For reluctance motor with magnistor changes parameter and the adaptability and the robustness of disturbance from improving in essence, realize that the suspending power under the diverse location carries out dynamic Decoupling Control of Load Torque, and then the runnability of raising reluctance motor with magnistor, realize real high-performance operation, need to adopt some new control technologys and new control method.
Summary of the invention
The purpose of this invention is to provide a kind of suspending power of reluctance motor with magnistor under diverse location that make and carry out dynamic decoupling, make it have good dynamic and static control performance, anti-parameter changes and strong neural network inverse decoupling controller and the building method of anti-load disturbance ability.
The technical scheme that realizes above-mentioned purpose is that the building method of the neural net reversed decoupling control of reluctance motor with magnistor is: the voltage source inverter, a common current-controlled voltage source inverter that forms of contrary Clarke Clark conversion (the Flow Control inverter that is called for short expansion) that at first adopts the control of current hysteresis ring is as the part of the nerve network reverse controller of structure thereafter; Then the Flow Control inverter of controlled reluctance motor with magnistor and expansion is formed composite controlled object, the controlled volume of composite controlled object is two radial positions; And then adopt static neural network to add integrator s
-1Construct the nerve network reverse of composite controlled object, and make nerve network reverse realize the inverse system function of composite controlled object by the weight coefficient of adjusting neural net; Next nerve network reverse is placed before the composite controlled object, nerve network reverse and composite controlled object are formed pseudo-linear system.Pseudo-linear system is equivalent to the integral linearity subsystem of two decoupling zeros, be respectively the linear subsystem of two second order integral forms, be two radial position subsystems of reluctance motor with magnistor correspondence, thereby make the suspending power under the diverse location realize dynamic decoupling; On this basis, the integrated approach that adopts a kind of linear system designs two radial position closed loop controllers respectively to the integration subsystem of two decoupling zeros; At last the Flow Control inverter of linear closed-loop controller, nerve network reverse and expansion is constituted nerve network reverse controller jointly and come reluctance motor with magnistor is controlled, realize diverse location suspending power realization dynamic Decoupling Control of Load Torque down.
The structure of controller is: by the neural network inverse decoupling controller that 3 parts of Flow Control inverter of nerve network reverse, linear closed-loop controller, expansion are formed, wherein formed the Flow Control inverter of expansion jointly by current hysteresis ring control voltage source inverter, Clarke Clark conversion; Form the linear closed-loop controller by two radial position controllers; First and second outputs of linear closed-loop controller connect first and second inputs of nerve network reverse respectively, first and second outputs of nerve network reverse connect first and second inputs of the Flow Control inverter of expansion respectively, and first, second of the Flow Control inverter of expansion and the 3rd output connect first, second and the 3rd input of reluctance motor with magnistor respectively.
Principle of the present invention is contrary by constructing neural network, control will be converted into to the control of the suspending power under the reluctance motor with magnistor diverse location, correspondingly just linear closed loop controller can be designed easily two radial position second order integral linearity subsystems.Owing to really realized dynamic Decoupling Control of Load Torque, thereby can obtain good suspension power adjusting function to the suspending power under the diverse location.Owing to adopted the neural net that does not rely on the controlled device Mathematical Modeling to realize the inverse system function, thereby improved robustness greatly to parameter variation and load disturbance.
The invention has the advantages that:
1. employing nerve network reverse, the control problem of complex nonlinear coupled system is converted into the control problem of simple two linear subsystems (radial position), further appropriate design linear closed-loop controller can obtain the runnability of high performance decoupling zero control and anti-load disturbance.
2. add the inverse system that integrator is realized composite controlled object with static neural network, the constructing neural network inverse controller is realized the control to the suspending power under the reluctance motor with magnistor diverse location, be completely free of the dependence of traditional control method, reduced parameter effectively and changed and the influence of load disturbance reluctance motor with magnistor for Mathematical Modeling.
The present invention can be used for constructing novel decoupling controller the suspending power under the reluctance motor with magnistor diverse location is carried out high performance control, not only in the High Speed System that with the reluctance motor with magnistor is power set, very high using value is arranged, and be in the High-speed Control system of power set at the magnetic suspension motor with other type, application prospect also is very wide.
Description of drawings
Fig. 1 is made of the structure chart of the Flow Control inverter 3 of expansion jointly coordinate transform (contrary Clarke Clark conversion) 2, current hysteresis ring control voltage source inverter 1.
Fig. 2 is the principle assumption diagram with reluctance motor with magnistor 4 levitation position of Flow Control inverter 3 drive controlling of expansion.
Fig. 3 is two inputs (input of the Flow Control inverter of expansion) of reluctance motor with magnistor levitation position control and the equivalent control block diagram (composite controlled object 5) of two outputs (radial position).
Fig. 4 is the schematic diagram and the isoboles thereof of the pseudo-linear system 7 of nerve network reverse 6 and composite controlled object 5 compound formations.
Fig. 5 is added to two current component signals that the Flow Control inverter input of expanding in the composite controlled object shown in Figure 35 is used to obtain the neural metwork training data.
The structure chart of the closed-loop control system that Fig. 6 is made up of linear closed-loop controller 8 and pseudo-linear system 7.
Fig. 7 is the complete principle block diagram that adopts 9 pairs of reluctance motor with magnistor levitation position 4 of neural network inverse decoupling controller to control.
Fig. 8 adopts DSP to form schematic diagram as apparatus of the present invention of neural network inverse decoupling controller.
Fig. 9 is to be the realization systems soft ware block diagram of the present invention of controller with DSP.
1, current hysteresis ring control voltage source inverter; 2, coordinate transform (contrary Clarke Clark conversion); 3, Kuo Zhan Flow Control inverter; 4, reluctance motor with magnistor; 5, composite controlled object; 6, nerve network reverse; 7, pseudo-linear system; 71, rotor radial location subsystem; 72, rotor radial location subsystem; 8, linear closed-loop controller; 81, radial position controller; 82, radial position controller; 9, neural network inverse decoupling controller; 10, position transducer; 11, DSP.
Embodiment
Embodiment of the present invention are: at first formed the Flow Control inverter of expansion jointly by current hysteresis ring control voltage source inverter, Clarke Clark conversion, the Flow Control inverter of this expansion will be as a part of whole neural network inverse decoupling controller.Secondly with the Flow Control inverter of expansion and the control of reluctance motor with magnistor levitation position as a composite controlled object, this composite controlled object equivalence is the quadravalence Differential Equation Model two phase coordinate systems under, the vectorial relative rank of system be 2,2}.Adopt the static neural network (static neural network is multitiered network MLN) of 6 input nodes, 2 output nodes to add 4 integrator (s
-1) construct the nerve network reverse of composite controlled object.And make nerve network reverse realize the inverse system function of composite controlled object by each weight coefficient of adjusting static neural network.Nerve network reverse is serially connected in before the composite controlled object, nerve network reverse and composite controlled object synthesize by two second order integration subsystem (s again
-2) i.e. the pseudo-linear systems that constitute of two radial position subsystems, thereby the control of the nonlinear multivariable systems of a complexity is converted into the control of two simple second-order integration subsystems.Two second order integration subsystems for decoupling zero adopt a kind of linear system integration method, as PID or POLE PLACEMENT USING etc., make two radial position controllers respectively and form the linear closed-loop controller.The final neural network inverse decoupling controller of being made up of 3 parts of Flow Control inverter of nerve network reverse, linear closed-loop controller, expansion that forms comes the reluctance motor with magnistor levitation position is controlled.According to different control requirements, can select different hardware and softwares to realize.
Concrete enforcement divides following 9 steps:
1. construct the Flow Control inverter of expansion.At first form coordinate transform by Clarke Clark conversion, afterwards this coordinate transform and current hysteresis ring control voltage source inverter commonly used are formed jointly the Flow Control inverter of expansion, the Flow Control inverter of this expansion is its input (as shown in Figure 1) with two stator current components.The voltage-controlled inverter of expansion will be as a part of whole nerve network reverse Synchronization Control frequency converter.
2. formation composite controlled object.Composite controlled object is formed in Flow Control inverter, the control of reluctance motor with magnistor levitation position of the expansion that structure is good, and this composite controlled object is its input with two stator current components, and two radial positions are output (as shown in Figure 2).
By analyze, equivalence and derivation, for the structure of nerve network reverse and learning training provide basis on the method.At first set up the Mathematical Modeling of each composite controlled object, i.e. the quadravalence differential equation under two phase coordinate systems, its vector relatively rank be 2,2}.Inverse system through provable this quadravalence Differential Equation Model of deriving exists, and can determine two second dervatives that input is radial position of its inverse system, and two outputs are respectively two inputs of composite controlled object.Need to prove, this step only provides basis on the method for the structure of following nerve network reverse and learning training, in concrete enforcement of the present invention, and this step, comprise theoretical proof and some corresponding equivalent transformations and derivation etc. that the composite controlled object inverse system is existed, can skip.
4. adopt static neural network to add four integrator constructing neural networks contrary (shown in the frame of broken lines of the left figure of Fig. 4).Wherein static neural network adopts 3 layers MLN network, and the input layer number is 6, and the hidden layer node number is 13, and output layer node number is 2, and the hidden neuron activation primitive uses S type hyperbolic tangent function
The neuron of output layer adopts pure linear function f (x)=x, and x is neuronic input, and the weight coefficient of static neural network will be determined in next step off-line learning.Add 4 integrator s with static neural network then with 6 input nodes, 2 output nodes
-1Constitute nerve network reverse (shown in the frame of broken lines of the left figure of Fig. 4), wherein first of static neural network is input as first input of nerve network reverse, and it is through first integrator s
-1Being output as second input of static neural network, is the 3rd input of static neural network again through second integrator; The 4th second input that is input as nerve network reverse of static neural network, it is through the 3rd integrator s
-1Being output as the 5th input of static neural network, is the 6th input of static neural network again through the 4th integrator.Static neural network is formed nerve network reverse with four integrators, and the output of static neural network is exactly the output of nerve network reverse.
5. adjust the weight coefficient of static neural network.(a) two current components that will be as shown in Figure 5 are added to the Flow Control inverter (being the input of composite controlled object) of expansion respectively with the form of input, gather reluctance motor with magnistor suspension radial position θ with 6 milliseconds sampling periods
α, θ
βWith current i
α, i
β, and preserve data { θ
α, θ
β, i
α, i
β.(b) two radial position signal off-lines are asked its single order and second dervative respectively, and signal is done standardization processing, the training sample set of composition neural net
θ
α,
θ
β, i
α *, i
β *.(c) adopt the error anti-pass BP algorithm that drives quantifier and learning rate changing that static neural network is trained, through 600 training, neural net output mean square error meets the demands less than 0.001, thereby has determined each weight coefficient of static neural network.
6. form two radial position subsystems.Constitute nerve network reverse (shown in the frame of broken lines among the left figure of Fig. 6) by the static neural network of having determined each weight coefficient and 4 integrators, nerve network reverse and composite controlled object compose in series pseudo-linear system (shown in the right figure of Fig. 6), this pseudo-linear system is made up of two radial position subsystems, thereby reached the decoupling zero between the radial position, complicated nonlinear multivariable systems control has been converted into the control of simple two single argument linear systems.
7. make the linear closed-loop controller.(shown in the right figure of Fig. 6) makes linear closed-loop controller (as shown in Figure 7) respectively to two radial position subsystems.The linear closed-loop controller adopts proportional plus integral plus derivative controller PID, POLE PLACEMENT USING or the most excellent method of quadratic performance in the lineary system theory to design, in the embodiment that the present invention provides, two radial position controllers have all been selected proportion differential PD controller for use, and its parameter tuning is that two radial position controllers are PD=200+22s.Whole system as shown in Figure 7.
8. formation neural network inverse decoupling controller.The Flow Control inverter of nerve network reverse, linear closed-loop controller, expansion is formed neural network inverse decoupling controller (as shown in big frame of broken lines among Fig. 7) jointly.Can require to adopt different hardware or software to realize according to different control.
Fig. 8 has provided the schematic diagram of a kind of specific embodiment of the present invention, and wherein nerve network reverse, closed loop controller, coordinate transform, the ring controller that stagnates are that dsp controller is realized by software by digital signal processor; The Flow Control inverter of expansion adopts Intelligent Power Module to realize.The system program block diagram as shown in Figure 9.
According to the above, just can realize the present invention.
Claims (4)
1, a kind of neural network inverse decoupling controller building method of reluctance motor with magnistor radial position is characterized in that the controlled reluctance motor with magnistor radial position (4) and the Flow Control inverter (3) of expansion are formed composite controlled object (5); And then adopt static neural network to add integrator s
-1Construct the nerve network reverse (6) of composite controlled object, and make nerve network reverse (6) realize the inverse system function of composite controlled object (5) by the weight coefficient of adjusting neural net; Then nerve network reverse (6) is placed composite controlled object (5) before, nerve network reverse (6) is formed pseudo-linear system (7) with composite controlled object (5), pseudo-linear system (7) is equivalent to the integral linearity subsystem of two decoupling zeros, is respectively the linear subsystem (71,72) of two radial positions; On this basis, the integrated approach that adopts a kind of linear system designs two radial position controllers (81,82) respectively to the integration subsystem of two decoupling zeros and constitutes linear closed-loop controller (8); At last the Flow Control inverter (3) of linear closed-loop controller (8), nerve network reverse (6) and expansion being constituted nerve network reverse Synchronization Control frequency converter (9) jointly comes decoupling zero control is carried out in the reluctance motor with magnistor radial position.
2, the neural network inverse decoupling controller building method of reluctance motor with magnistor radial position according to claim 1 is characterized in that each weight coefficient of static neural network (61) determines that method is with step excitation signal i
α *And i
β *Be added to the input of composite controlled object (5); Gather two radial position θ
α, θ
βWith two stator phase current i
α, i
βWith two radial position θ
α, θ
βOff-line is asked its single order and second dervative respectively, and signal is done standardization processing, forms the training sample set of neural net
θ
α,
θ
β, i
α *, i
β *; Static neural network (61) is trained, thus each weight coefficient of definite static neural network (61).
3, the neural network inverse decoupling controller of reluctance motor with magnistor radial position, the structure that it is characterized in that controller is: by the neural network inverse decoupling controller that 3 parts of Flow Control inverter of nerve network reverse, linear closed-loop controller, expansion are formed, wherein formed the Flow Control inverter of expansion jointly by current hysteresis ring control voltage source inverter, Clarke Clark conversion; Form the linear closed-loop controller by two radial position controllers; First and second outputs of linear closed-loop controller connect first and second inputs of nerve network reverse respectively, first and second outputs of nerve network reverse connect first and second inputs of the Flow Control inverter of expansion respectively, and first, second of the Flow Control inverter of expansion and the 3rd output connect first, second and the 3rd input of reluctance motor with magnistor respectively.
4, the neural network inverse decoupling controller of reluctance motor with magnistor radial position according to claim 1 is characterized in that the static neural networks (61) with 6 input nodes, 2 output nodes that are configured to use of nerve network reverse (6) add 4 integrator s
-1Constitute the nerve network reverse (6) with 2 input nodes, 2 output nodes, wherein: first of static neural network is input as first input of nerve network reverse, and it is through first integrator s
-1Being output as second input of static neural network, is the 3rd input of static neural network again through second integrator; The 4th second input that is input as nerve network reverse of static neural network, it is through the 3rd integrator s
-1Being output as the 5th input of static neural network, is the 6th input of static neural network through the 4th integrator again, and static neural network is formed nerve network reverse with four integrators, and the output of static neural network is exactly the output of nerve network reverse.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CNB2005100380995A CN1314193C (en) | 2005-03-11 | 2005-03-11 | Magnetic levitation switch reluctance motor radial neural network reversed decoupling controller and method for constructing same |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CNB2005100380995A CN1314193C (en) | 2005-03-11 | 2005-03-11 | Magnetic levitation switch reluctance motor radial neural network reversed decoupling controller and method for constructing same |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN1655438A true CN1655438A (en) | 2005-08-17 |
| CN1314193C CN1314193C (en) | 2007-05-02 |
Family
ID=34894410
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CNB2005100380995A Expired - Fee Related CN1314193C (en) | 2005-03-11 | 2005-03-11 | Magnetic levitation switch reluctance motor radial neural network reversed decoupling controller and method for constructing same |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN1314193C (en) |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN100426158C (en) * | 2005-12-22 | 2008-10-15 | 安徽工业大学 | Indirect decoupling method of multi variable system based on nerve network reverse idontification and reverso control |
| CN101814892A (en) * | 2010-03-04 | 2010-08-25 | 江苏大学 | Support vector machine inverse system composite controller based on bearingless synchronous reluctance motor |
| CN101697469B (en) * | 2009-10-28 | 2011-08-24 | 南京航空航天大学 | Method for controlling position-less sensor of bearing-less switched reluctance motor |
| CN101710808B (en) * | 2009-11-27 | 2012-01-04 | 北京航空航天大学 | Radial decoupling method of rotor system of magnetically suspended control moment gyroscope |
| CN102509152A (en) * | 2011-11-08 | 2012-06-20 | 南京航空航天大学 | Switched reluctance motor on-line modeling method based RBF neural network |
| CN102769424A (en) * | 2012-06-18 | 2012-11-07 | 江苏大学 | Support vector machine inverse-based bearingless asynchronous motor control system |
| CN103414432A (en) * | 2013-07-12 | 2013-11-27 | 江苏大学 | Construction method of hybrid electric vehicle belt driven starter generator controller |
| CN103940392A (en) * | 2014-04-17 | 2014-07-23 | 江苏大学 | Rotor position/displacement self-detection method of magnetic-levitation switched reluctance motor |
| CN104767449A (en) * | 2015-03-02 | 2015-07-08 | 江苏大学 | Bearing-free asynchronous motor RBF neural network self-adaptive inverse decoupling control and parameter identification method |
| CN106059425A (en) * | 2016-02-16 | 2016-10-26 | 国家电网公司 | Control method for dual-winding magnetic suspension switched reluctance generator |
| CN109217766A (en) * | 2018-09-26 | 2019-01-15 | 河南科技大学 | The independent reversed decoupling control system of induction-type bearingless motor |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1032411A (en) * | 1987-10-09 | 1989-04-12 | 上海工业大学 | Decouple-controlled frequency-converting speed-regulating system |
-
2005
- 2005-03-11 CN CNB2005100380995A patent/CN1314193C/en not_active Expired - Fee Related
Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN100426158C (en) * | 2005-12-22 | 2008-10-15 | 安徽工业大学 | Indirect decoupling method of multi variable system based on nerve network reverse idontification and reverso control |
| CN101697469B (en) * | 2009-10-28 | 2011-08-24 | 南京航空航天大学 | Method for controlling position-less sensor of bearing-less switched reluctance motor |
| CN101710808B (en) * | 2009-11-27 | 2012-01-04 | 北京航空航天大学 | Radial decoupling method of rotor system of magnetically suspended control moment gyroscope |
| CN101814892A (en) * | 2010-03-04 | 2010-08-25 | 江苏大学 | Support vector machine inverse system composite controller based on bearingless synchronous reluctance motor |
| CN101814892B (en) * | 2010-03-04 | 2012-05-23 | 江苏大学 | Support vector machine inverse system composite controller based on bearingless synchronous reluctance motor |
| CN102509152A (en) * | 2011-11-08 | 2012-06-20 | 南京航空航天大学 | Switched reluctance motor on-line modeling method based RBF neural network |
| CN102769424A (en) * | 2012-06-18 | 2012-11-07 | 江苏大学 | Support vector machine inverse-based bearingless asynchronous motor control system |
| CN102769424B (en) * | 2012-06-18 | 2016-12-21 | 江苏大学 | Based on the induction-type bearingless motor control system that support vector machine is inverse |
| CN103414432B (en) * | 2013-07-12 | 2015-11-18 | 江苏大学 | A kind of building method of hybrid electric vehicle belt driven starter generator controller |
| CN103414432A (en) * | 2013-07-12 | 2013-11-27 | 江苏大学 | Construction method of hybrid electric vehicle belt driven starter generator controller |
| CN103940392A (en) * | 2014-04-17 | 2014-07-23 | 江苏大学 | Rotor position/displacement self-detection method of magnetic-levitation switched reluctance motor |
| CN103940392B (en) * | 2014-04-17 | 2016-08-17 | 江苏大学 | The rotor-position of a kind of magnetic suspension switched reluctance motor/displacement self-sensing method |
| CN104767449A (en) * | 2015-03-02 | 2015-07-08 | 江苏大学 | Bearing-free asynchronous motor RBF neural network self-adaptive inverse decoupling control and parameter identification method |
| CN104767449B (en) * | 2015-03-02 | 2018-04-24 | 江苏大学 | Self-bearings motors RBF neural adaptive inversion decoupling control and parameter identification method |
| CN106059425A (en) * | 2016-02-16 | 2016-10-26 | 国家电网公司 | Control method for dual-winding magnetic suspension switched reluctance generator |
| CN109217766A (en) * | 2018-09-26 | 2019-01-15 | 河南科技大学 | The independent reversed decoupling control system of induction-type bearingless motor |
| CN109217766B (en) * | 2018-09-26 | 2021-07-23 | 河南科技大学 | Independent inverse decoupling control system of bearingless asynchronous motor |
Also Published As
| Publication number | Publication date |
|---|---|
| CN1314193C (en) | 2007-05-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN1314193C (en) | Magnetic levitation switch reluctance motor radial neural network reversed decoupling controller and method for constructing same | |
| CN100433537C (en) | Method for controlling bearing-less AC asynchronous motor neural network inverse decoupling controller | |
| CN100536311C (en) | Neural network generalized inverse permanent magnetism synchronous machine decoupling controller structure method without bearing | |
| CN101814892B (en) | Support vector machine inverse system composite controller based on bearingless synchronous reluctance motor | |
| CN1212702C (en) | Neural network reversal control frequency converter of induction motor and structure method | |
| CN101741297B (en) | Radial position fuzzy compensation inverse control method and device for bearingless synchronous reluctance motor | |
| Kotb et al. | Speed control and torque ripple minimization of SRM using local unimodal sampling and spotted hyena algorithms based cascaded PID controller | |
| CN101299581B (en) | Neural network generalized inverse coordination control frequency transformer for two induction machines and construction method thereof | |
| CN102790577B (en) | Constructing method for suspended subsystem controller of bearingless permanent magnet synchronous motor | |
| CN102130647B (en) | Speed-sensor-less construction method for detecting rotating speed of bearing-less asynchronous motor | |
| CN102097986A (en) | Construction method for neural network generalized inverse decoupling controller of bearing-free synchronous reluctance motor | |
| CN1885708A (en) | Constructing method for bearingless synchronous reluctance motor feedforward compensation controller | |
| CN1216454C (en) | Construction method for contradictorily synchronous control frequency-converter of two-phase induction motor neural net | |
| CN102790578B (en) | Manufacturing method of bearingless asynchronous motor neural network generalized inverse decoupling controller | |
| CN110594213A (en) | Electro-hydraulic servo actuator capable of realizing long-stroke high-frequency loading and control method | |
| CN202004708U (en) | Bearingless brushless direct current motor neural network alpha-order inverse controller | |
| CN1226824C (en) | Method for structuring neural network reversal composite controller of variable frequency speed control system with constant ratio of voltage and frequency | |
| CN115085609B (en) | Control method for sliding mode system of single-winding magnetic suspension motor | |
| CN1216455C (en) | Method for structuring mervo network inverse synchronous controller of tow-frequenct changing speed regulating motor system | |
| CN102790576B (en) | A kind of building method of bearing-free permanent magnet synchronous motor decoupling controller | |
| Gencer | Analysis of Speed/Position Controller Based on Several Types of a Fuzzy Logic for Travelling Wave Ultrasonic Motor | |
| Gencer | A comparative speed/position control technique based Fuzzy Logic control for travelling wave ultrasonic motor | |
| Li et al. | Brushless DC Motor control strategy for electric vehicles | |
| CN111240196B (en) | Control method and system of electro-hydrostatic actuator | |
| CN1292525C (en) | Neural network inverse controller for controlled series capacitance compensating device and configuration method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| EE01 | Entry into force of recordation of patent licensing contract |
Assignee: Zhenjiang Jiangda Science and Technologies Co., Ltd. Assignor: Jiangsu University Contract fulfillment period: 2008.10.7 to 2018.10.7 contract change Contract record no.: 2008320000865 Denomination of invention: Magnetic levitation switch reluctance motor radial neural network reversed decoupling controller and method for constructing same Granted publication date: 20070502 License type: Exclusive license Record date: 20081013 |
|
| LIC | Patent licence contract for exploitation submitted for record |
Free format text: EXCLUSIVE LICENSE; TIME LIMIT OF IMPLEMENTING CONTACT: 2008.10.7 TO 2018.10.7; CHANGE OF CONTRACT Name of requester: ZHENJIANG CITY,JIANGDA TECHNOLOGY CO., LTD. Effective date: 20081013 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20070502 Termination date: 20150311 |
|
| EXPY | Termination of patent right or utility model |