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Neural network analysis of the magnetic bearing systems

  • Hongya Fu*
  • , Pingfan Liu
  • , Qingchun Zhang
  • , Guodong Li
  • *Corresponding author for this work
  • Harbin Institute of Technology
  • RIGOL

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In order to overcome the system nonlinear instability and uncertainty inherent in magnetic bearing systems, two PID neural network controllers (BP-based and GA-based) are designed and trained to emulate the operation of a complete system. Through the theoretical deduction and simulation results, the principles for the parameters choice of two neural network controllers are given. The feasibility of using the neural network to control nonlinear magnetic bearing systems with un-known dynamics is demonstrated. The robust performance and reinforcement learning capability in controlling magnetic bearing systems are compared between two PID neural network controllers.

Original languageEnglish
Title of host publicationApplied Mechanics and Mechanical Engineering
Pages190-196
Number of pages7
DOIs
StatePublished - 2010
Event2010 International Conference on Applied Mechanics and Mechanical Engineering, ICAMME 2010 - Changsha, China
Duration: 8 Sep 20109 Sep 2010

Publication series

NameApplied Mechanics and Materials
Volume29-32
ISSN (Print)1660-9336
ISSN (Electronic)1662-7482

Conference

Conference2010 International Conference on Applied Mechanics and Mechanical Engineering, ICAMME 2010
Country/TerritoryChina
CityChangsha
Period8/09/109/09/10

Keywords

  • BP
  • GA
  • Magnetic bearing
  • PID neural network

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