Manufacturing method of radial fuzzy neural network generalized inverse controller of bearingless asynchronous motor

Abstract

The invention discloses a manufacturing method of a fuzzy neural network generalized inverse controller on a radial position of a bearingless asynchronous motor, and the manufacturing method comprises the following steps that a fuzzy neural network with six input nodes and two output nodes and four linear links are used for forming a fuzzy neural network generalized inverse with two input nodes and two output nodes, each parameter and each weight coefficient of the fuzzy neural network are adjusted to make the fuzzy neural network generalized inverse realize a generalized inverse system function of a composite controlled target; the fuzzy neural network generalized inverse is serially connected in front of the composite controlled target to form a generalized pseudo-linear system; and the fuzzy neural network generalized inverse is serially connected in front of the composite controlled target, the fuzzy neural network generalized inverse, a Park inverter, a Clark inverter and a current tracking-type inverter collectively form the controller to realize the open-loop linear control of the nonlinear system on the radial position of the bearingless asynchronous motor, and the stable suspension running of the bearingless asynchronous motor can be guaranteed without designing a complicated closed-loop controller.

Description

technical field [0001] The invention relates to a construction method of a fuzzy neural network generalized inverse controller for the radial position of a bearingless asynchronous motor, which is suitable for high-performance control of the radial position of a bearingless asynchronous motor and belongs to the technical field of electric drive control equipment. Background technique [0002] Bearingless asynchronous motors have the advantages of no friction, no wear, no lubrication, reliable operation, simple structure, and low cost. It has a very broad application prospect in special occasions such as hard disk drives and aerospace. [0003] Bearingless asynchronous motor is a multivariable, nonlinear and time-varying strongly coupled complex system. In order to realize its stable levitation operation, it must be controlled by nonlinear decoupling. The decoupling control of bearingless asynchronous motor mainly includes vector control, inverse system method, neural networ...

AI Technical Summary

Problems solved by technology

Although the bearingless asynchronous motor under the vector control strategy based on the dynamic model has the advantages of good dynamic performance and wide speed regulation range, because the vector control method includes the rotor parameters and load torque of the bearingless asynchronous motor, the perturbation of the rotor parameters The sudden change of load torque will make the system robustness worse and affect the actual control effect of the system

The inverse system method is to transform the complex non-system into a simple linear system, and use linear theory to analyze and design the linear controller in a wide working area without losing the controllability and accuracy of the system. The method needs to obtain an accurate mathematical model of the bearingless asynchronous motor while realizing the linear decoupling of the system, so it is difficult to apply it in engineering

Although the neural network inverse system method effectively solves the difficulty in obtaining the inverse model in the application of the inverse system method, the pseudo-linear system obtained after the linearization and decoupling of the neural network inverse system method is still open-loop unstable, requiring complex design. closed-loop controller, and the neural network based on empirical risk minimization has defects such as over-learning, local minima, and the selection of type and structure is too dependent on experience, which makes the actual effect of the neural network inverse system method not good.

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