Construction method for neural network Alpha-order inverse controller of bearing-free brushless DC motor

A brushed DC motor and neural network inverse technology is applied in the construction field of the neural network α-order inverse controller of the bearingless brushless DC motor to achieve the effect of a practical structure, ensuring stable suspension and operation, and shortening the axial length of the rotor

Inactive Publication Date: 2011-05-11
JIANGSU UNIV
View PDF7 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, there has not been any published literature on the design of neural network inverse decoupling controllers for bearingless brushless DC motors; due to the particularity of the structure of bearingless brushless DC motors, it is necessary to deduce the mathematics of its torque system and suspension system in sections model, and its reversibility is analyzed in sections, and its corresponding neural network inverse system structure is also different from other bearingless motors

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Construction method for neural network Alpha-order inverse controller of bearing-free brushless DC motor
  • Construction method for neural network Alpha-order inverse controller of bearing-free brushless DC motor
  • Construction method for neural network Alpha-order inverse controller of bearing-free brushless DC motor

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0020] see Figure 1-5 , the present invention first takes two PWM inverters 2, 3 and the bearingless brushless DC motor 1 as a whole to form a composite controlled object 4, and then uses a static neural network 5 to add an integrator s -1 To construct the neural network inverse 6 of the compound controlled object 4, and make the neural network inverse 6 realize the inverse system function of the compound controlled object 4 by adjusting the weight coefficient of the neural network, and then place the neural network inverse 6 in the compound controlled object 4 Previously, the neural network inverse 6 and the compound controlled object 4 constituted a pseudo-linear system 7, which was equivalent to two pseudo-linear subsystems of the second-order integral type of position and a pseudo-linear subsystem of the second-order integral type of velocity, On this basis, two position controllers 81, 82 and one speed controller 83 are designed respectively for the three integral subsy...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a construction method for a neural network Alpha-order inverse controller of a bearing-free brushless DC motor. Firstly, two PWM (Pulse-Width Modulation) inverters and the bearing-free brushless DC motor serve as a whole to form a compounded to-be-controlled target; an adopted static neural network and six integrators construct the neural network inverse of the compounded to-be-controlled target; then the neural network inverse is placed in front of the compounded to-be-controlled target and is connected with the compounded to-be-controlled target in series to form a pseudo linear system, and two designed position controllers and a speed controller form a linear close-loop controller; and finally, the linear close-loop controller, the neural network inverse and the two PWM inverters are connected in series in sequence to form the neural network Alpha-order inverse controller of the bearing-free brushless DC motor. The construction method realizes the independent control of the radial displacement and the rotational speed of the bearing-free brushless DC motor rotor, ensures the steady suspension and operation of the motor rotor, and enables the bearing-free brushless DC motor to obtain favorable dynamic and static performance and load disturbance resistance.

Description

technical field [0001] The invention relates to a neural network α-order inverse controller for a bearingless brushless DC motor, which is suitable for high-performance control of the bearingless brushless DC motor and belongs to the technical field of electric drive control equipment. Background technique [0002] Brushless DC motor combines the characteristics of DC motor and AC motor. It has the advantages of good speed regulation performance, easy starting, load starting, long life, etc., and it is easy to maintain, low noise, and does not have a series of problems caused by brushes. . The bearingless brushless DC motor uses the magnetic field force to realize the suspension of the motor rotor, which not only has the advantages of the brushless DC motor, but also has the frictionless, wear-free, lubrication and sealing of the magnetic bearing motor, high speed, high precision, and long life. Long characteristics, with great potential engineering application value. [0...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): H02P6/08H02P21/00H02P23/00H02P23/14H02P27/06
Inventor 朱熀秋张婷婷潘伟
Owner JIANGSU UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products