Construction method for neural network generalized inverse decoupling controller of bearing-free synchronous reluctance motor

Abstract

The invention discloses a construction method for a neural network generalized inverse decoupling controller of a bearing-free synchronous reluctance motor, which comprises the steps: taking two Park inverse converting type inverters, two Clark inverse converting type inverters and two direct current tracking type inverters as a wholly-formed composite controlled object after the two Park inverse converting type inverters, the two Clark inverse converting type inverters and the two direct current tracking type inverters are respectively and sequentially connected with one another in series and before the two Park inverse converting type inverters, the two Clark inverse converting type inverters and the two direct current tracking type inverters are connected with the bearing-free synchronous reluctance motor; forming a generalized imitative linear system before a constructed neural network generalized inverse is connected with the composite controlled object in series, and forming a linear closed loop controller by two position controllers and a speed controller; and jointly forming the neural network generalized inverse decoupling controller by the means that the linear closed loop controller, the neural network generalized inverse, the two Park inverse converting type inverters, the two Clark inverse converting type inverters and the two direct current tracking type inverters are respectively and sequentially connected with one another in series. The independent decoupling control between the electromagnetic torque and the radial levitation force and the independent decoupling control of the radial levitation force between two components on the vertical direction are realized according to the closed ring control and the PID (proportion integration differentiation) parameter adjustment, and the control performance of the bearing-free synchronous reluctance motor is obviously improved.

Description

Construction method of neural network generalized inverse decoupling controller for bearingless synchronous reluctance motor technical field The invention relates to a neural network generalized inverse decoupling controller for a bearingless synchronous reluctance motor, which is used for high-performance control of a bearingless synchronous reluctance motor and belongs to the technical field of electric drive control equipment. Background technique The bearingless synchronous reluctance motor meets the requirements of modern industry for high-speed, non-lubricated, friction-free, and maintenance-free high-performance drive motors. It is a new type of synchronous reluctance motor with excellent performance of magnetic bearings and Motors have broad application prospects in special electrical transmission fields such as machine tool electric spindles, turbomolecular pumps, centrifuges, compressors, electromechanical energy storage, and aerospace. Bearingless synchronous re...

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Problems solved by technology

In addition, its decoupling control method is to use the neural network generalized inverse directly as a nonlinear open-loop controller, and the control effect is not good.

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