Direct decentralized neural control for nonlinear MIMO magnetic levitation system

Syuan Yi Chen, Faa Jeng Lin*, Kuo Kai Shyu

*此作品的通信作者

研究成果: 雜誌貢獻期刊論文同行評審

25 引文 斯高帕斯(Scopus)

摘要

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 trajectories. 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 and the convergence analysis of the tracking error using discrete-type Lyapunov function is provided. 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 various periodic reference trajectories simultaneously in different operating conditions effectively.

原文英語
頁(從 - 到)3220-3230
頁數11
期刊Neurocomputing
72
發行號13-15
DOIs
出版狀態已發佈 - 2009 八月

ASJC Scopus subject areas

  • 電腦科學應用
  • 認知神經科學
  • 人工智慧

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