ニューラルネットワークを併用したセルフチューニングPID制御系の一設計 A Design of Self-Tuning PID Controllers Fused with a Neural Network

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著者

    • 山本 透 YAMAMOTO Toru
    • 岡山県立大学情報工学部 Faculty of Computer Science and Systems Engineering, Okayama Prefectural University
    • 沖 俊任 OKI Toshitaka
    • 岡山県立大学情報工学部 Faculty of Computer Science and Systems Engineering, Okayama Prefectural University

抄録

In recent years, the structure and the mechanism of the human brain have been clarified partially, and their knowledges are formulated as neural network systems. Furthermore, there have been applications of neural networks to pattern matching problems, pattern recognitions, learning controls and so on. Especially, neural network techniques have widely used in adaptive and learning control schemes for nonlinear systems. However, generally, it costs a lot of time for learning in the case applied in control systems. Furthermore, the physical meaning of neural networks constructed as a result, is not obvious. In this paper, a design method of self-tuning PID controllers is proposed, which has a fusional structure of self-tuning and neural network schemes. This method enables us to understand a physical meaning of the control parameters, and also to adjust PID gains quickly. Finally, in order to show the effectiveness of the proposed self-tuning PID control scheme, a numerical simulation example is illustrated.

収録刊行物

  • 計測自動制御学会論文集  

    計測自動制御学会論文集 34(7), 682-688, 1998-07-31 

    The Society of Instrument and Control Engineers

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各種コード

  • NII論文ID(NAID)
    10002478495
  • NII書誌ID(NCID)
    AN00072392
  • 本文言語コード
    JPN
  • 資料種別
    ART
  • ISSN
    04534654
  • NDL 記事登録ID
    4528471
  • NDL 雑誌分類
    ZM11(科学技術--科学技術一般--制御工学)
  • NDL 請求記号
    Z14-482
  • データ提供元
    CJP書誌  CJP引用  NDL  J-STAGE 
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