Nonlinear control for MIMO magnetic levitation system using direct decentralized neural networks

Syuan-Yi Chen, Faa Jeng Lin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

A direct modified Elman neural networks (MENNs)-based decentralized controller is proposed to control the magnets of a nonlinear and unstable multi-input multi-output (MIMO) levitation system for the tracking of reference trajectory. First, the operating principles of a magnetic levitation system with two moving magnets are introduced. Then, due to the exact dynamic model of the MIMO magnetic levitation system is not clear, two MENNs are combined to be a direct MENN-based decentralized controller to deal with the highly nonlinear and unstable MIMO magnetic levitation system. Moreover, the connective weights of the MENNs are trained online by back-propagation (BP) methodology. Based on the direct and decentralized concepts, the computational burden is reduced and the controller design is simplified. Furthermore, the experimental results show that the proposed control scheme can control the magnets to track periodic sinusoidal reference trajectory simultaneously in different operating conditions effectively.

Original languageEnglish
Title of host publication2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2009
Pages1763-1768
Number of pages6
DOIs
Publication statusPublished - 2009 Nov 4
Event2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2009 - Singapore, Singapore
Duration: 2009 Jul 142009 Jul 17

Other

Other2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2009
CountrySingapore
CitySingapore
Period09/7/1409/7/17

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ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this

Chen, S-Y., & Lin, F. J. (2009). Nonlinear control for MIMO magnetic levitation system using direct decentralized neural networks. In 2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2009 (pp. 1763-1768). [5229811] https://doi.org/10.1109/AIM.2009.5229811