Manufacturing method of bearingless asynchronous motor neural network generalized inverse decoupling controller

A kind of neural network and asynchronous motor technology, applied in the direction of motor generator control, AC motor control, electronic commutation motor control, etc., can solve problems such as inability to open-loop control, open-loop instability, and difficulties in differential geometry and inverse system methods

Active Publication Date: 2015-06-10
HUAWEI TEHCHNOLOGIES CO LTD
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Problems solved by technology

Bearingless asynchronous motor is a multi-variable, strongly coupled nonlinear complex system, its motor parameters change obviously with various working conditions, coupled with the existence of load disturbance, the change of radial suspension force when the stator and rotor are eccentric, and The influence of unmodeled dynamics such as magnetic circuit saturation makes the differential geometry and inverse system methods encounter difficulties in practical applications
In order to make up for the deficiencies of the differential geometric control and inverse system methods, the neural network inverse control method is adopted, but when the neural network inverse control method is used, although the original nonlinear system can be linearized and decoupled into a single-input and single-output system with a linear relationship Integral subsystems (pseudo-linear composite systems), but these integral subsystems are open-loop unstable, so the neural network inversion cannot simply be used as the only controller to "open-loop control" the bearingless asynchronous motor, but must Design an additional closed-loop controller, so the control is more complicated
[0004] In order to essentially solve the complex closed-loop control problem of the bearingless asynchronous motor system, and at the same time ensure the control performance indicators of the bearingless asynchronous motor, such as steady-state tracking accuracy and dynamic response speed, new control methods and new Control Technology

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  • Manufacturing method of bearingless asynchronous motor neural network generalized inverse decoupling controller
  • Manufacturing method of bearingless asynchronous motor neural network generalized inverse decoupling controller
  • Manufacturing method of bearingless asynchronous motor neural network generalized inverse decoupling controller

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Embodiment Construction

[0017] The present invention firstly consists of two Clark inverse transforms, two current tracking inverters and a bearingless asynchronous motor load as a whole to form a composite controlled object, which is equivalent to a 6th-order differential equation in a stationary coordinate system model, the relative order of the system vectors is {2,2,1,1}. A neural network generalized inverse of a compound controlled object with 10 input nodes and 4 output nodes is formed by using a static neural network (3-layer network) with 10 input nodes and 4 output nodes and linear links such as integral and inertia. And by adjusting the weights of the static neural network, the generalized inverse of the neural network realizes the function of the generalized inverse system of the compound controlled object. Then, the neural network generalized inverse is connected in series before the compound controlled object, and the neural network generalized inverse and the compound controlled object ...

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Abstract

The invention discloses a manufacturing method of a bearingless asynchronous motor neural network generalized inverse decoupling controller, and the manufacturing method comprises following steps that a static neural network of ten input nodes and four output nodes and six linear links are used for forming a neural network generalized inverse of four input nodes and four output nodes, the neural network generalized inverse is arranged in front of a composite controlled target to form a generalized pseudo-linear system, the generalized pseudo-linear system is decoupled and linearized into two displacement two-step pseudo-linear subsystems, one rotation speed one-step pseudo-linear subsystem and one magnetic chain one-step pseudo-linear subsystem; and the neural network generalized inverse, two Clark inverse transformers and two current tracking-type inverters collectively form the neural network generalized inverse decoupling controller, not only is the dynamic decoupling between a radial displacement system and a rotation speed system and between the radial suspension forces of the bearingless asynchronous motor realized, but also the controller can be used as a nonlinear open-loop controller to be directly used, and the stable suspension running of a bearingless asynchronous motor rotor can be guaranteed.

Description

technical field [0001] The invention belongs to the technical field of electric drive control equipment, and is a neural network-based generalized inverse bearingless asynchronous motor control system, which controls the bearingless asynchronous motor and is suitable for high-performance control of the bearingless asynchronous motor. Background technique [0002] The bearingless asynchronous motor has the advantages of simple structure, solid and reliable, small and uniform air gap, and low cost. It can also use ordinary cage rotors, has high mechanical strength, can run at ultra-high speed, and has low cogging pulsation torque. With a wide field weakening range, it is one of the most promising bearingless motors. However, the bearingless asynchronous motor has complex electromagnetic relations, and is a multivariable, nonlinear, and strongly coupled system. There is not only coupling between the motor speed subsystem and the magnetic chain subsystem, but also coupling betwe...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H02P21/00H02P27/06
Inventor 孙晓东陈龙李可杨泽斌朱熀秋
Owner HUAWEI TEHCHNOLOGIES CO LTD
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