小口径トンネルロボットのニューラル形最適ゲインオートチューニング
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- Material Type
- 記事
- Author/Editor
- 青島 伸一武田 幸喜薮田 哲郎
- Periodical title
- 日本機械学会論文集. C編
- No. or year of volume/issue
- 58(546) 1992.02
- Volume
- 58
- Issue
- 546
- Pages
- p499～505
- Publication date of volume/issue (W3CDTF)
- 1992-02
- ISSN (Periodical Title)
- 0387-5024
- ISSN-L (Periodical Title)
- 0387-5024
- Publication (Periodical Title)
- 東京 : 日本機械学会 ; 1979-2010
- Place of Publication (Country Code)
- JP
- Text Language Code
- jpn
- NDLC
- Target Audience
- 一般
- Article Type
- 記事分類: 制御工学
- Holding library
- 国立国会図書館
- Call No.
- Z16-1056
- Data Provider (Database)
- 国立国会図書館 : 国立国会図書館雑誌記事索引
- Bibliographic ID (NDL)
- 3751359
- Bibliographic Record Category (NDL)
- 632
- Summary, etc.
- This paper describes the autotuning of feedback gain for a small tunnelling robot. We have already proposed a directional control method in that the head angle of the control input is the sum of the deviation multiplied by feedback gain K<SUB>p</SUB> and the angular deviation multiplied by feedback gain K<SUB>a</SUB>. In this paper, we use a neural network to obtain feedback gains K<SUB>p</SUB> and K<SUB>a</SUB>. The input of the neural network is the initial deviation and initial angular deviation. The output of the neural network is the feedback gains K<SUB>p</SUB> and K<SUB>a</SUB>. This neural network learns from deviation error. The optimum gains obtained by the proposed method agreed with the optimum gain obtained by trial and error. The neural network which can apply to any initial deviation was formed by using plural deviations. Moreover, this method can tune the optimum gains to any design line. These results showed the validity of this method.
- DOI
- 10.1299/kikaic.58.499
- Access Restrictions
- インターネット公開
- Data Provider (Database)
- 科学技術振興機構 : J-STAGE
- Summary, etc.
- This paper describes the autotuning of feedback gain for a small tunnelling robot. We have already proposed a directional control method in that the head angle of the control input is the sum of the deviation multiplied by feedback gain K<SUB>p</SUB> and the angular deviation multiplied by feedback gain K<SUB>a</SUB>. In this paper, we use a neural network to obtain feedback gains K<SUB>p</SUB> and K<SUB>a</SUB>. The input of the neural network is the initial deviation and initial angular deviation. The output of the neural network is the feedback gains K<SUB>p</SUB> and K<SUB>a</SUB>. This neural network learns from deviation error. The optimum gains obtained by the proposed method agreed with the optimum gain obtained by trial and error. The neural network which can apply to any initial deviation was formed by using plural deviations. Moreover, this method can tune the optimum gains to any design line. These results showed the validity of this method.
- DOI
- 10.1299/kikaic.58.499
- Related Material (URI)
- Data Provider (Database)
- 国立情報学研究所 : CiNii Research
- Original Data Provider (Database)
- Japan Link Center雑誌記事索引データベースCrossrefCiNii ArticlesCiNii Articles
- Bibliographic ID (NDL)
- 3751359
- NAID
- 130004082978110002380188