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

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...

AI Technical Summary

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

Images