Method for observing left inverse state of neural network of permanent magnet synchronous motor

Abstract

The invention discloses a method for observing a left inverse state of a neural network of a permanent magnet synchronous motor, and belongs to the technical field of motor drive control. An observer of the left inverse state of the neural network is constructed through the method for observing the left inverse state of the neural network to achieve the state observation of a multivariable and strong-coupled time-varying nonlinear system of a permanent magnet synchronous motor system without a sensor. According to the method for observing the left inverse state of the neural network of the permanent magnet synchronous motor, the observation problem of a complex system mutually coupled by the current, the voltage and the speed of a stator is translated into a simple observation problem of a unit mapping system, therefore, high-precision observation of the rotate speed of the permanent magnet synchronous motor and the angular position of a rotor is achieved, and the interference of system noise is overcome. An observation system is made to have excellent dynamic performance, static performance and anti-interference and high-precision tracking performance.

Description

technical field [0001] The invention belongs to the technical field of motor drive control, and more precisely, the invention relates to a left inverse state observation method of a neural network of a permanent magnet synchronous motor. Background technique [0002] As we all know, due to the characteristics of high power density, high torque inertia ratio, high reliability and other characteristics of permanent magnet synchronous motor (PMSM), its application in the field of motor drive is more and more extensive. The state observer can enable the PMSM control system to obtain the rotor angular position and rotational speed information of the PMSM system state quantity without installing a position or speed sensor, which improves the reliability of the system and saves manufacturing costs at the same time. Therefore, scholars at home and abroad have successively developed the speed sensorless control technology, and proposed different motor speed and position observation s...

AI Technical Summary

Problems solved by technology

Because the traditional left inverse soft sensor 22 in this method is composed of a pure differential link and a traditional neural network, and the introduction of a pure differential link will be difficult for systems with large state changes and serious system noise, such as permanent magnet synchronous motor control systems. Causes amplified noise, causes oscillations, cannot accurately and quickly identify the required state quantity

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