CN102790577A - Constructing method for suspended subsystem controller of bearingless permanent magnet synchronous motor - Google Patents
Constructing method for suspended subsystem controller of bearingless permanent magnet synchronous motor Download PDFInfo
- Publication number
- CN102790577A CN102790577A CN2012102757360A CN201210275736A CN102790577A CN 102790577 A CN102790577 A CN 102790577A CN 2012102757360 A CN2012102757360 A CN 2012102757360A CN 201210275736 A CN201210275736 A CN 201210275736A CN 102790577 A CN102790577 A CN 102790577A
- Authority
- CN
- China
- Prior art keywords
- neural network
- fuzzy neural
- inverse
- input
- output
- 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
- Magnetic Bearings And Hydrostatic Bearings (AREA)
- Control Of Multiple Motors (AREA)
- Control Of Ac Motors In General (AREA)
- Feedback Control In General (AREA)
Abstract
The invention discloses a constructing method for a suspended subsystem controller of a bearingless permanent magnet synchronous motor. A complex controlled object is constructed by a Park inverse transformer, a Clark inverse transformer, a current tracking inverter and a suspended subsystem model of the bearingless permanent magnet synchronous motor as a whole; a fuzzy neural network inverter of the complex controlled object is constructed by one fuzzy neural network and four integrators; then the fuzzy neural network inverter is arranged in front of the complex controlled object to form a pseudo-linear system; and the pseudo-linear system is linearly decoupled into two single-input and single-output second-order displacement subsystems, two displacement controllers are designed for the two second-order integral displacement subsystems to construct a linear closed-loop controller, and a fuzzy neural network inverter controller is finally constructed by the linear closed-loop controller, the fuzzy neural network inverter, the Park inverse transformer, the Clark inverse transformer and the current tracking inverter. High-performance control on the suspended subsystem of the bearingless permanent magnet synchronous motor can be realized.
Description
Technical field
The present invention is a kind of building method of bearing-free permanent magnet synchronous motor suspension subsystem fuzzy neural network inverse controller, is applicable to the high performance control of bearing-free permanent magnet synchronous motor suspension subsystem, belongs to the technical field of Electric Drive control appliance.
Background technology
Bearing-free permanent magnet synchronous motor is according to the similitude of conventional motors structure and magnetic bearing structure; The magnetic suspension bearing winding that produces radial load is in the same place with permanent-magnetic synchronous motor stator winding lap wound; Make rotor have rotation and suspending power simultaneously, realize the no bearingization of motor.Bearing-free permanent magnet synchronous motor not only has magnetic suspension bearing and need not lubricate, not have the machinery friction, not have advantages such as wearing and tearing, high speed and super precision; And have that traditional permagnetic synchronous motor power factor is high, efficient is high, volume is little, in light weight, advantage such as control characteristic is good, thereby it is with a wide range of applications at special dimensions such as accurate digital control system lathe, flywheel energy storage, biological medicine, semiconductor manufacturing, Aero-Space.
Be different from common alternating current machine, bearing-free permanent magnet synchronous motor be non-linear a, close coupling, multiple-input and multiple-output, the time complication system that becomes, make conventional PID control method usually attend to one thing and lose sight of another, can't reach gratifying control effect.Therefore, the control of bearing-free permanent magnet synchronous motor is a very stubborn problem, demands exploring new theory urgently, new method breaks through.Particularly under different operating conditions; Bearing-free permanent magnet synchronous motor receives influence that load disturbance and parameter change very greatly; Realize its stable suspersion and controlled rotation; Must carry out dynamic Decoupling Control of Load Torque to the suspension subsystem of bearing-free permanent magnet synchronous motor, seek its control law under different radial positions.
For from improving robustness and the adaptability of bearing-free permanent magnet synchronous motor in essence to load disturbance and parameter time varying; Realize the dynamic Decoupling Control of Load Torque of suspension subsystem under different radial positions; Thereby improve the suspension operation performance of bearing-free permanent magnet synchronous motor; Realize its high-quality operation, must adopt new control strategy.
Summary of the invention
The purpose of this invention is to provide a kind of bearing-free permanent magnet synchronous motor suspension subsystem radial load under different radial positions that can make and carry out non-linear decoupling zero control; Make it have good quiet, dynamic control performance, and can simplified system the building method of bearing-free permanent magnet synchronous motor suspension subsystem fuzzy neural network inverse controller of control.
The technical scheme that the present invention adopts is to adopt following steps successively: 1) compose in series composite controlled object successively by Park inverse transformation, Clark inverse transformation, current track inverter and controlled bearing-free permanent magnet synchronous motor suspension subsystem; 2) set up the Mathematical Modeling of composite controlled object; Add 4 integrators with fuzzy neural network and constitute fuzzy neural network inverse with 2 input nodes, 2 output nodes with 6 input nodes, 2 output nodes; Wherein, First input of fuzzy neural network inverse is as first input of fuzzy neural network, and it is through an integrator
s -1Be output as second input of fuzzy neural network, again through second integrator
s -1Be output as the 3rd input of fuzzy neural network; Second input of fuzzy neural network inverse is as the 4th input of fuzzy neural network, and it is through an integrator
s -1Be output as the 5th input of fuzzy neural network, again through second integrator
s -1Be output as the 6th input of fuzzy neural network, the output of fuzzy neural network is the output of fuzzy neural network inverse; 3) adjustment and definite parameters of fuzzy neural network and weight coefficient; Make fuzzy neural network inverse realize the inverse system function of composite controlled object; Fuzzy neural network inverse is series at composite controlled object forms pseudo-linear system before, pseudo-linear system is two the single output of single input displacement second order integration subsystems by the linearisation decoupling zero; 4) respectively two corresponding two displacement controllers of displacement second order integration subsystem design are constituted the linear closed-loop controller; 5) the linear closed-loop controller is serially connected in before the fuzzy neural network inverse, constitutes the fuzzy neural network inverse controller jointly by linear closed-loop controller, fuzzy neural network inverse, Park inverse transformation, Clark inverse transformation and current track inverter.
The invention has the beneficial effects as follows:
1, with the linearisation decoupling zero characteristic of method of inverse; Combine to the approximation capability of non linear system and to the adaptive capacity of system parameter variations with fuzzy neural network; The present invention has provided bearing-free permanent magnet synchronous motor suspension subsystem fuzzy neural network inverse controller and building method, thereby has solved traditional parsing inverse system bottleneck problem that inversion model is difficult to obtain in practical application.
2, through the structure fuzzy neural network inverse; To be converted into control to suspend this multivariable of subsystem, control non-linear, the close coupling time-varying system of bearing-free permanent magnet synchronous motor to two simple displacement second order integration subsystems; Can use the method design closed loop controller of conventional linear control theory easily, thereby realize high performance control bearing-free permanent magnet synchronous motor suspension subsystem.
Description of drawings
Fig. 1 is made up of the sketch map of composite controlled object 15 Park inverse transformation 11, Clark inverse transformation 12, current track inverter 13 and controlled bearing-free permanent magnet synchronous motor suspension subsystem 14;
Fig. 2 is the structural representation of the fuzzy neural network inverse 22 that is made up of 5 layers of fuzzy neural network 21 of 6 inputs nodes, 2 output nodes and 4 integrators;
Fig. 3 is the sketch map and the isoboles thereof of the pseudo-linear system 3 of fuzzy neural network inverse 22 and composite controlled object 15 compound formations;
Fig. 4 is the closed-loop control system structure chart that is made up of linear closed-loop controller 4 and pseudo-linear system 3;
Fig. 5 is bearing-free permanent magnet synchronous motor suspension subsystem fuzzy neural network inverse controller 5 theory diagrams.
Embodiment
Embodiment of the present invention are: at first form composite controlled object by Park inverse transformation, Clark inverse transformation, current track inverter and controlled bearing-free permanent magnet synchronous motor suspension subsystem; This composite controlled object equivalence is 4 rank Differential Equation Models under the two cordic phase rotators system; The relative rank of system vector be 2,2}.Adopt fuzzy neural network (5 layer network) and 4 fuzzy neural network inverses that the linear elements formation has the composite controlled object of 2 input nodes, 2 output nodes of 6 input nodes, 2 output nodes.And through adjusting the inverse system function that parameters of fuzzy neural network and weight coefficient make fuzzy neural network inverse realization composite controlled object.Fuzzy neural network inverse is series at composite controlled object forms pseudo-linear system before, pseudo-linear system is two the single output of single input integration subsystems by the linearisation decoupling zero, is respectively two displacement second order integration subsystems.On this basis, constitute the linear closed-loop controller to two displacement controllers of two second order integration subsystem design respectively.At last linear closed-loop controller, fuzzy neural network inverse, Park inverse transformation, Clark inverse transformation and current track inverter being constituted the fuzzy neural network inverse controller jointly comes bearing-free permanent magnet synchronous motor suspension subsystem is carried out Nonlinear Dynamic decoupling zero control.
7 steps below practical implementation divides:
1, forms composite controlled object 15.Be connected in series successively by Park inverse transformation 11, Clark inverse transformation 12, current track inverter 13 and controlled bearing-free permanent magnet synchronous motor suspension subsystem 14; Form composite controlled object 15 by Park inverse transformation 11, Clark inverse transformation 12, current track inverter 13 and controlled bearing-free permanent magnet synchronous motor suspension subsystem 14, as shown in Figure 1.This composite controlled object 15 with
, 
Two current signals are as input, with the rotor radial displacement
x,
yAs output.
2, through analyze, equivalence and derivation, for the structure and the learning training of fuzzy neural network inverse 22 provides the basis on the method.At first based on the bearing-free permanent magnet synchronous motor operation principle; Set up the Mathematical Modeling of bearing-free permanent magnet synchronous motor suspension subsystem; Through coordinate transform and linear amplification, obtain the Mathematical Modeling of composite controlled object 15, i.e. the 4 rank differential equations under the two cordic phase rotators system; Its vector relatively rank be 2,2}.Can prove that through deriving this 4 rank differential equation is reversible; Be that inverse system exists; And 2 of can confirm its inverse system are input as
;
; 2 are output as
,
.Thereby can construct fuzzy neural network inverse 22, for learning training provides the basis on the method, as shown in Figure 2.
3, adopt fuzzy neural network 21 and 4 integrators to construct fuzzy neural network inverse 22; Fuzzy neural network 21 adopts 5 layer self-adapting neural fuzzy inference systems (abbreviating fuzzy neural network as); Input number of nodes is 6, and output layer node number is 2, and error criterion is chosen the mean square error of sample; The membership function of input and output variable all adopts bell shaped function; And 15 membership functions are got in each input, and the output function type is linear, and the parameter of fuzzy neural network 21 and weight coefficient are confirmed in next step off-line learning.Then adopt fuzzy neural network 21 to add 4 linear elements and construct fuzzy neural network inverse 22, wherein: first input of fuzzy neural network inverse 22 with 2 input nodes, 2 output nodes with 6 input nodes, 2 output nodes
As first input of fuzzy neural network 21, it is through an integrator
s -1Be output as
, be second input of fuzzy neural network 21, again through second integrator
s -1Be output as
, be the 3rd input of fuzzy neural network 21; Second input of fuzzy neural network inverse 22
As the 4th input of fuzzy neural network 21, it is through an integrator
s -1Be output as
, be the 5th input of fuzzy neural network 21, again through second integrator
s -1Be output as
, be the 6th input of fuzzy neural network 21.Fuzzy neural network 21 is formed fuzzy neural network inverse 22 with 4 integrators, and the output of fuzzy neural network 21 is exactly the output of fuzzy neural network inverse 22.
4, confirm the parameter and the weight coefficient of fuzzy neural network 21: 1) with the step excitation signal
,
Be added to the input of composite controlled object 15, gather the rotor radial displacement of bearing-free permanent magnet synchronous motor suspension subsystem radial position
x,
y2) with two rotor displacements
x,
yOff-line is asked its single order, second dervative respectively, and signal is done standardization processing, the training sample set of composition fuzzy neural network 21
,
,
,
,
,
,
, 
.3) adopt hybrid algorithm that fuzzy neural network 21 is trained, through about 750 times training, fuzzy neural network 21 output mean square errors meet the demands less than 0.001, thereby confirm each parameter and the weight coefficient of fuzzy neural network 21.
5, form two displacement second order integration subsystems.Fuzzy neural network 21 and 4 integrators by confirming each parameter and weight coefficient constitute fuzzy neural network inverse 22; Fuzzy neural network inverse 22 is formed pseudo-linear system 3 with composite controlled object 15 polyphones; This pseudo-linear system 3 is two the single output of single input displacement second order integration subsystems by the linearisation decoupling zero; Form by two displacement second order integration subsystems; Thereby realize bearing-free permanent magnet synchronous motor suspension subsystem in the non-linear decoupling zero between the radial load under the different radial positions, be converted into the control of simple two single argument linear systems to the control of Complex Nonlinear System, as shown in Figure 3.
6, the linear closed loop controller 4 of design.To two radial positions of two bearing-free permanent magnet synchronous motor suspension subsystems, to two corresponding two displacement controllers 41,42 of displacement second order integration subsystem design, constitute linear closed-loop controller 4 respectively by two displacement controllers 41,42.Two displacement controllers are all selected the PID controller for use among the present invention, and its parameter is adjusted according to the working control object, and is as shown in Figure 4.
7, constitute the fuzzy neural network inverse controller.Linear closed-loop controller 4 is serially connected in before the fuzzy neural network inverse 22; Form fuzzy neural network inverse controller 5 jointly by linear closed-loop controller 4, fuzzy neural network inverse 22, Park inverse transformation 11, Clark inverse transformation 12, current track inverter 13, as shown in Figure 5.
According to the above, just can realize the present invention.
Claims (2)
1. the building method of a bearing-free permanent magnet synchronous motor suspension subsystem controller is characterized in that adopting successively following steps:
1) composes in series composite controlled object (15) successively by Park inverse transformation (11), Clark inverse transformation (12), current track inverter (13) and controlled bearing-free permanent magnet synchronous motor suspension subsystem (14);
2) set up the Mathematical Modeling of composite controlled object (15); Add 4 integrators with fuzzy neural network (21) and constitute fuzzy neural network inverse (22) with 2 input nodes, 2 output nodes with 6 input nodes, 2 output nodes; Wherein, First input of fuzzy neural network inverse (22) is as first input of fuzzy neural network (21), and it is through an integrator
s -1Be output as second input of fuzzy neural network (21), again through second integrator
s -1Be output as the 3rd input of fuzzy neural network (21); Second input of fuzzy neural network inverse (22) is as the 4th input of fuzzy neural network (21), and it is through an integrator
s -1Be output as the 5th input of fuzzy neural network (21), again through second integrator
s -1Be output as the 6th input of fuzzy neural network (21), the output of fuzzy neural network (21) is the output of fuzzy neural network inverse (22);
3) adjustment and definite parameters of fuzzy neural network and weight coefficient; Make fuzzy neural network inverse (22) realize the inverse system function of composite controlled object (15); Fuzzy neural network inverse (22) is series at composite controlled object (15) forms pseudo-linear system (3) before, pseudo-linear system (3) is two the single output of single input displacement second order integration subsystems by the linearisation decoupling zero;
4) respectively two corresponding two displacement controllers of displacement second order integration subsystem design (41,42) are constituted linear closed-loop controller (4);
5) linear closed-loop controller (4) is serially connected in fuzzy neural network inverse (22) before, constitutes fuzzy neural network inverse controller (5) jointly by linear closed-loop controller (4), fuzzy neural network inverse (22), Park inverse transformation (11), Clark inverse transformation (12) and current track inverter (13).
2. building method according to claim 1 is characterized in that: the parameter and the Determination of Weight Coefficient method of said fuzzy neural network (21) are: with the step excitation signal
, 
Be added to the input of composite controlled object (15), the radial displacement of gathering bearing-free permanent magnet synchronous motor suspension subsystem
x,
y, and with radial displacement
x,
yOff-line is asked its single order, second dervative respectively, and signal is done standardization processing, the training sample set of composition fuzzy neural network (21)
,
,
,
,
,
,
, , fuzzy neural network (21) is trained definite parameter and weight coefficient.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201210275736.0A CN102790577B (en) | 2012-08-06 | 2012-08-06 | Constructing method for suspended subsystem controller of bearingless permanent magnet synchronous motor |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201210275736.0A CN102790577B (en) | 2012-08-06 | 2012-08-06 | Constructing method for suspended subsystem controller of bearingless permanent magnet synchronous motor |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN102790577A true CN102790577A (en) | 2012-11-21 |
| CN102790577B CN102790577B (en) | 2015-06-10 |
Family
ID=47155891
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201210275736.0A Active CN102790577B (en) | 2012-08-06 | 2012-08-06 | Constructing method for suspended subsystem controller of bearingless permanent magnet synchronous motor |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102790577B (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103425052A (en) * | 2013-08-21 | 2013-12-04 | 江苏大学 | Radical active magnetic bearing controller and construction method |
| CN103647481A (en) * | 2013-12-13 | 2014-03-19 | 江苏大学 | Adaptive inverse controller construction method for bearingless permanent magnetic synchronous motor radial position nerve network |
| CN103647487A (en) * | 2013-08-13 | 2014-03-19 | 江苏大学 | Bearingless permanent magnet motor suspension system control method based on dual inverse models |
| CN104362925B (en) * | 2013-10-21 | 2017-05-03 | 江苏大学 | Method for structuring simplified active disturbance rejection controllers with bearingless asynchronous motor radial position system |
| CN106849793A (en) * | 2017-03-01 | 2017-06-13 | 西安交通大学 | A kind of Over Electric Motor with PMSM fuzzy Neural Network Control System |
| CN109600083A (en) * | 2018-11-19 | 2019-04-09 | 江苏大学 | Two degrees of freedom bearing-free permanent magnet synchronous motor suspending power subsystem decoupled controller |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5656911A (en) * | 1994-12-27 | 1997-08-12 | Fuji Electric Company | Circuit for driving permanent-magnet synchronous motor using proportional controller |
| CN202043069U (en) * | 2011-04-12 | 2011-11-16 | 江苏大学 | Decoupling controller of five-degree-freedom bearingless synchronous reluctance motor |
-
2012
- 2012-08-06 CN CN201210275736.0A patent/CN102790577B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5656911A (en) * | 1994-12-27 | 1997-08-12 | Fuji Electric Company | Circuit for driving permanent-magnet synchronous motor using proportional controller |
| CN202043069U (en) * | 2011-04-12 | 2011-11-16 | 江苏大学 | Decoupling controller of five-degree-freedom bearingless synchronous reluctance motor |
Non-Patent Citations (2)
| Title |
|---|
| 孙晓东等: "基于神经网络逆系统理论无轴承异步电动机解耦控制", 《电工技术学报》 * |
| 孙晓东等: "无轴承永磁同步电机径向悬浮力动态解耦控制", 《电机与控制学报》 * |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103647487A (en) * | 2013-08-13 | 2014-03-19 | 江苏大学 | Bearingless permanent magnet motor suspension system control method based on dual inverse models |
| CN103647487B (en) * | 2013-08-13 | 2016-01-20 | 江苏大学 | Based on the permanent-magnetic electric machine with bearing suspension system control method of two inversion model |
| CN103425052A (en) * | 2013-08-21 | 2013-12-04 | 江苏大学 | Radical active magnetic bearing controller and construction method |
| CN103425052B (en) * | 2013-08-21 | 2016-05-25 | 江苏大学 | A kind of building method of radially active magnetic bearings control device |
| CN104362925B (en) * | 2013-10-21 | 2017-05-03 | 江苏大学 | Method for structuring simplified active disturbance rejection controllers with bearingless asynchronous motor radial position system |
| CN103647481A (en) * | 2013-12-13 | 2014-03-19 | 江苏大学 | Adaptive inverse controller construction method for bearingless permanent magnetic synchronous motor radial position nerve network |
| CN103647481B (en) * | 2013-12-13 | 2016-03-02 | 江苏大学 | Bearing-free permanent magnet synchronous motor radial position neural Network Adaptive Inversion Control device building method |
| CN106849793A (en) * | 2017-03-01 | 2017-06-13 | 西安交通大学 | A kind of Over Electric Motor with PMSM fuzzy Neural Network Control System |
| CN106849793B (en) * | 2017-03-01 | 2019-03-01 | 西安交通大学 | A kind of Over Electric Motor with PMSM fuzzy Neural Network Control System |
| CN109600083A (en) * | 2018-11-19 | 2019-04-09 | 江苏大学 | Two degrees of freedom bearing-free permanent magnet synchronous motor suspending power subsystem decoupled controller |
Also Published As
| Publication number | Publication date |
|---|---|
| CN102790577B (en) | 2015-06-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN101814892B (en) | Support vector machine inverse system composite controller based on bearingless synchronous reluctance motor | |
| CN102790577B (en) | Constructing method for suspended subsystem controller of bearingless permanent magnet synchronous motor | |
| CN100433537C (en) | Method for controlling bearing-less AC asynchronous motor neural network inverse decoupling controller | |
| Yang et al. | Study on active disturbance rejection control of a bearingless induction motor based on an improved particle swarm optimization–genetic algorithm | |
| CN102361429A (en) | Bearing-free asynchronous motor control method based on neural network inverse system theory | |
| Qiang et al. | PMSM servo control system design based on fuzzy PID | |
| CN105577058A (en) | Novel fuzzy active disturbance rejection controller based five-phase fault-tolerant permanent magnet motor speed control method | |
| CN102790581B (en) | Constructing method for robust controller for radial position of bearingless asynchronous motor | |
| Yang et al. | Fractional-order sliding mode control for a bearingless induction motor based on improved load torque observer | |
| CN109217766A (en) | The independent reversed decoupling control system of induction-type bearingless motor | |
| Gao et al. | A Sliding Mode Control with Nonlinear Fractional Order PID Sliding Surface for the Speed Operation of Surface‐Mounted PMSM Drives Based on an Extended State Observer | |
| CN102136822B (en) | Five-DOF (freedom of degree) bearingless synchronous reluctance motor decoupling controller and construction method thereof | |
| CN102097986A (en) | Construction method for neural network generalized inverse decoupling controller of bearing-free synchronous reluctance motor | |
| CN102790580B (en) | The building method of induction-type bearingless motor SVMs inverse decoupling controller | |
| CN202043069U (en) | Decoupling controller of five-degree-freedom bearingless synchronous reluctance motor | |
| CN103595321A (en) | Method for constructing decoupling controller of five-degree-of-freedom alternating-current active magnetic bearing | |
| CN102510253A (en) | Sensorless controller of bearingless synchronous reluctance motor and method for controlling sensorless controller | |
| CN102790578B (en) | Manufacturing method of bearingless asynchronous motor neural network generalized inverse decoupling controller | |
| CN202004708U (en) | Bearingless brushless direct current motor neural network alpha-order inverse controller | |
| CN101958685A (en) | Nonlinear inverse decoupling controller for bearingless synchronous reluctance motor and construction method thereof | |
| CN201928221U (en) | Neural net generalized inverse decoupling controller for bearingless synchronous reluctance motor | |
| CN102790583B (en) | The building method of the radial generalized inverse internal mode controller of bearing-free permanent magnet synchronous motor | |
| Sun et al. | Artificial neural networks inverse control of 5 degrees of freedom bearingless induction motor | |
| CN102790576A (en) | Construction method for bearing-less permanent-magnet synchronous motor decoupling controller | |
| CN110048657A (en) | Induction-type bearingless motor Neural Network Inverse System decoupling control 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 | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20200629 Address after: 215500 No.13, Caotang Road, Changshu, Suzhou, Jiangsu Province Patentee after: Changshu intellectual property operation center Co.,Ltd. Address before: Zhenjiang City, Jiangsu Province, 212013 Jingkou District Road No. 301 Patentee before: JIANGSU University |
|
| TR01 | Transfer of patent right | ||
| CP02 | Change in the address of a patent holder |
Address after: 215500 5th floor, building 4, 68 Lianfeng Road, Changfu street, Changshu City, Suzhou City, Jiangsu Province Patentee after: Changshu intellectual property operation center Co.,Ltd. Address before: 215500 No.13, Caotang Road, Changshu, Suzhou, Jiangsu Province Patentee before: Changshu intellectual property operation center Co.,Ltd. |
|
| CP02 | Change in the address of a patent holder | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20230621 Address after: 215500 No. 20, Zhongtian Road, Zone E, Changkun Industrial Park, Shajiabang, Changshu, Suzhou, Jiangsu Patentee after: Jiangsu Qicun Energy Saving Technology Group Co.,Ltd. Address before: 215500 5th floor, building 4, 68 Lianfeng Road, Changfu street, Changshu City, Suzhou City, Jiangsu Province Patentee before: Changshu intellectual property operation center Co.,Ltd. |
|
| TR01 | Transfer of patent right |