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

• 資料種別: 記事
• タイトル: 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
• 著者・編者: 青島 伸一; 武田 幸喜; 薮田 哲郎
• 著者標目: 青島 伸一; 武田 幸喜; 薮田 哲郎
• タイトル（掲載誌）: 日本機械学会論文集. C編
• 巻号年月日等（掲載誌）: 58(546) 1992.02
• 掲載巻: 58
• 掲載号: 546
• 掲載ページ: p499～505
• 掲載年月日（W3CDTF）: 1992-02
• ISSN（掲載誌）: 0387-5024
• ISSN-L（掲載誌）: 0387-5024
• 出版事項（掲載誌）: 東京 : 日本機械学会 ; 1979-2010
• 出版地（国名コード）: JP
• 本文の言語コード: jpn
• NDLC: ZN11
• 対象利用者: 一般
• 記事種別、記事分類: 記事分類: 制御工学
• 所蔵機関: 国立国会図書館
• 請求記号: Z16-1056
• 連携機関・データベース: 国立国会図書館 : 国立国会図書館雑誌記事索引; https://ndlsearch.ndl.go.jp
• 書誌ID（NDLBibID）: 3751359; http://id.ndl.go.jp/bib/3751359
• 整理区分コード: 632

• 要約等: 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
• オンライン閲覧公開範囲: インターネット公開
• 連携機関・データベース: 科学技術振興機構 : J-STAGE; http://www.jstage.jst.go.jp

• 要約等: 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; https://doi.org/10.1299/kikaic.58.499
• 関連情報（URI）: http://id.ndl.go.jp/bib/3751359; https://ndlsearch.ndl.go.jp/books/R000000004-I3751359; http://www.jstage.jst.go.jp/article/kikaic1979/58/546/58_546_499/_pdf
• 連携機関・データベース: 国立情報学研究所 : CiNii Research; https://cir.nii.ac.jp/
• 提供元機関・データベース: Japan Link Center; https://japanlinkcenter.org/top; 雑誌記事索引データベース; https://ndlsearch.ndl.go.jp; Crossref; https://www.crossref.org; CiNii Articles
• 書誌ID（NDLBibID）: 3751359; http://id.ndl.go.jp/bib/3751359
• NII論文ID: 130004082978; 110002380188