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Method for controlling bearing-less AC asynchronous motor neural network inverse decoupling controller

An AC asynchronous motor, neural network inverse technology, applied in the direction of AC motor control, motor parameter estimation/correction, control system, etc., to achieve the effect of practical structure, dynamic decoupling, and compact structure

Inactive Publication Date: 2008-11-12
JIANGSU UNIV
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Problems solved by technology

The idea of ​​the neural network inverse controller used in the above two invention patents to control the motor is related to this patent to a certain extent, but there are essential differences in the structure, mathematical model, control method, control difficulty and requirements of various motors. For bearingless asynchronous The rotor flux observation and controller design of the motor, currently there are no relevant patents and literature

Method used

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  • Method for controlling bearing-less AC asynchronous motor neural network inverse decoupling controller
  • Method for controlling bearing-less AC asynchronous motor neural network inverse decoupling controller
  • Method for controlling bearing-less AC asynchronous motor neural network inverse decoupling controller

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Embodiment Construction

[0022]The embodiment of the present invention is: firstly based on the prototype body of the bearingless AC asynchronous motor, and then composed of two Clark inverse transforms, two current tracking inverters and the load of the bearingless AC asynchronous motor as a whole to form a composite controlled object, the The compound controlled object is equivalent to a 6-order differential equation model in the static coordinate system, and the relative order of the system vector is {2, 2, 1, 1}. A static neural network (3-layer network) with 10 input nodes and 4 output nodes plus 6 integrators -1 Construct the neural network inverse of a compound plant with 10 input nodes and 4 output nodes. And by adjusting the weights of the static neural network, the neural network inversely realizes the inverse system function of the compound controlled object. Then the neural network inverse is connected before the compound controlled object, and the neural network inverse and the compound ...

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Abstract

The control method for a NN reverse decoupling controller of bearingless ac asynchronous motor comprises: composing the controlled target with two Clark inverse transforms, two current track inverters, the said motor and its load; according to corresponding inverse system, making up the NN inverse by a static NN and an integrator with learning algorithm to series connect between composite targets and form the pseudolinear system; designing linear close-loop controller as the method on linear system; finally, connecting the controller and NN inverse to form the objective controller with former transforms and inverters. This invention has well control performance.

Description

technical field [0001] The invention is a control method of a multi-variable nonlinear bearingless AC asynchronous motor neural network inverse decoupling controller, which is suitable for high-performance control of the bearingless AC asynchronous motor. Bearingless AC asynchronous motor inherits the characteristics of no lubrication, no wear and no mechanical noise of the magnetic bearing motor, and is used in special electrical transmission fields such as machine tool spindles, turbomolecular pumps, centrifuges, compressors, electromechanical energy storage, and aerospace. The utility model has wide application prospects and belongs to the technical field of electric drive control equipment. Background technique [0002] Bearingless AC asynchronous motor is a kind of multivariable, nonlinear and strongly coupled controlled object, and its radial position and speed are difficult to be accurately controlled by external signals. In order to achieve stable suspension and ope...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H02P23/00H02P23/14G05B13/02H02P23/12
Inventor 朱熀秋周阳刘贤兴张腾超方亮赵筱赫
Owner JIANGSU UNIV
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