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Permanent magnet synchronous motor parameter identification method based on artificial neural network

A permanent magnet synchronous motor and artificial neural network technology, applied in the field of servo control system, to achieve the effect of good adaptability and simple structure

Inactive Publication Date: 2015-02-25
DONGHUA UNIV +1
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004] Advanced control technologies such as adaptive control, robust control, intelligent control, and sliding mode variable structure control have been successfully applied in PMSM control, but these control methods have certain limitations.

Method used

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  • Permanent magnet synchronous motor parameter identification method based on artificial neural network
  • Permanent magnet synchronous motor parameter identification method based on artificial neural network
  • Permanent magnet synchronous motor parameter identification method based on artificial neural network

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Embodiment Construction

[0028] In order to make the present invention more comprehensible, preferred embodiments are described in detail below with accompanying drawings. It should be understood that these examples are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art may make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the appended claims of the present application.

[0029] The invention proposes a parameter identification method of a permanent magnet synchronous motor based on an artificial neural network, and realizes real-time monitoring of motor parameters. During the design process, the discretization model of the system established is: i q (k)=αi q (k-1)+βu q (k-1)+γω(k-1), where,

[0030] First, a two-layer linear neur...

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Abstract

The invention relates to a permanent magnet synchronous motor parameter identification method based on an artificial neural network. The two-layer linear neural network is adopted for the method, and a motor vector control strategy is used in cooperation. First, the neural network is used for carrying out off-line training on acquired motor current rotating speed data, after the control accuracy meeting the requirement is achieved, a neural network weight obtained through off-line training serves as an initial value for on-line learning of the neural network, and then on-line learning adjustment is carried out on a system. The weight of the neural network is adjusted on line through a steepest descent method so that the output of the neural network can be close to an actual value, and then the stator resistance and the quadrature axis and direct axis inductance of a motor and the estimated value of rotor flux linkage are obtained.

Description

technical field [0001] The invention relates to the technical field of servo control systems, in particular to an artificial neural network-based parameter identification method for permanent magnet synchronous motors. Background technique [0002] The speed of permanent magnet synchronous motor (PMSM) is strictly synchronized with the frequency of the power supply. It has small size, light weight, high power factor, high efficiency, high magnetic flux density, fast dynamic response, high reliability, no maintenance, and strict speed synchronization. And wide speed range and other advantages. After more than 20 years of development, the permanent magnet synchronous AC servo system has been widely used in the field of motion control. [0003] The rapid development of modern high-performance CNC machine tools and robots requires their drive systems to have higher precision and better control performance, which puts forward high-precision control strategy requirements for PMSM...

Claims

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Application Information

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IPC IPC(8): H02P21/14G06N3/02
Inventor 周武能王菊平刘峙飞孔超波田波丁曹凯
Owner DONGHUA UNIV
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