Bearing-free asynchronous motor control method based on neural network inverse system theory

A neural network inverse and asynchronous motor technology, applied in the field of bearingless asynchronous motor control, can solve problems such as the difficulty of accurate modeling of the system and the difficulty of applying the analytical inverse system method, so as to overcome interference, realize high-performance control, and suppress parameter input. moving effect

Inactive Publication Date: 2012-02-22
JIANGSU UNIV
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Problems solved by technology

As a complex nonlinear object, the rotor parameters of the bearingless asynchronous motor change significantly with the working conditions, coupled with the existence of load d

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  • Bearing-free asynchronous motor control method based on neural network inverse system theory
  • Bearing-free asynchronous motor control method based on neural network inverse system theory
  • Bearing-free asynchronous motor control method based on neural network inverse system theory

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Embodiment Construction

[0025] In the present invention, firstly, the bearingless asynchronous motor 1, two current-regulated inverters 3, 4, two sets of Park inverse transforms 23, 24, two sets of Clark inverse transforms 21, 22, and flux linkage observer 27 are combined as a whole The controlled object 7, the composite controlled object is equivalent to a sixth-order differential equation model in the rotor field-oriented coordinate system, and the relative order of the vector of the system is {2,2,1,1}. A fuzzy neural network with 10 input nodes and 4 output nodes plus 6 integrators ( ) to construct the neural network inverse system 9 of the compound controlled object. And by training the fuzzy neural network 8, the neural network inverse system 9 realizes the inverse system function of the compound controlled object 7. Then the neural network inverse system 9 is connected in series before the composite controlled object 7, and the neural network inverse system 9 and the composite controlled obj...

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Abstract

The invention discloses a bearing-free asynchronous motor control method based on a neural network inverse system theory. A composite controlled object is composed of two sets of Park inverse transformation, two sets of Clark inverse transformation, two sets of current regulating inverters, a flux linkage observer, and a bearing-free asynchronous motor; a fuzzy neural network and integrators form a fuzzy neural network inverse system; and the fuzzy neural network inverse system is in series connection with the composite controlled object; besides, the bearing-free asynchronous motor is decoupled into a pseudo linear system comprising two displacement subsystems, a rotating speed subsystem and a rotor flux linkage subsystem; and the obtained pseudo linear system is introduced into internal model control to form closed-loop control. According to the invention, the control precision is high and there is high robustness on an external disturbance, a parameter change and a modeling error, thereby realizing high-performance control on a bearing-free asynchronous motor.

Description

technical field [0001] The invention belongs to the technical field of electric drive control equipment, in particular to a bearingless asynchronous motor control method based on neural network inverse system theory. Background technique [0002] The bearingless asynchronous motor is a nonlinear, multi-variable, and strongly coupled system. Realizing the dynamic decoupling control between its electromagnetic torque and radial levitation force is the key to the stable levitation and rotation of the motor. [0003] At present, the control methods of bearingless asynchronous motors mainly include vector control and feedback linearization control. Among them, the vector control strategy based on the torque winding air-gap magnetic field orientation can realize the separate control of the torque and air-gap flux linkage of the bearingless asynchronous motor, but because the air-gap flux is still related to the torque current, it has not realized the real Decoupling control i...

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Application Information

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IPC IPC(8): H02P21/08G06N3/08
Inventor 刘贤兴王正齐孙宇新
Owner JIANGSU UNIV
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