Autotuning of Feedback Gains Using a Neural Network for a Small Tunneling Robot

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概要

• 論文の詳細を見る

• This paper describes the autotuning of feedback gains for a small tunneling robot. We have already proposed the directional control method wherein the head angle of the control input is the sum of the deviation multiplied by feedback gain K_p and the angular deviation multiplied by feedback gain K_a. In this paper, we used a neural network to obtain feedback gains K_p and K_a. The input of the neural network is an initial deviation and an initial angular deviation. The output of the neural network is the feedback gains K_p and K_a. This neural network learns from the deviation errors. The neural network which can be applied to any initial deviation was formed by using plural initial deviations in learning. Moreover this method can tune optimum gains to any design line. These results showed the validity of the proposed autotuning method.

• 一般社団法人日本機械学会の論文
• 1993-12-15

著者

• Takeda K

  Tohoku Univ. Miyagi Jpn

• Yabuta Tetsuro

  Ntt Access Network Systems Laboratories

• Takeda Kouki

  NTT Telecommunication Field Systems R & D Center

• Yabuta Tetsuro

  NTT Telecommunication Field Systems R & D Center

• Aoshima Shin'ichi

  Faculty Of Engineering Ibaraki University

関連論文

• Dynamic Simplified Model and Autotuning of Feedback Gain for Directional Control Using a Neural Network for a Small Tunneling Robot
• Autotuning of Feedback Gains Using a Neural Network for a Small Tunneling Robot
• A Multiunit Wire-Mobile Robot that Avoids Obstacles and Transfers to Branch Wires

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