Neural type optimum gain auto-tuning method of small diameter tunnel robot

Abstract

Abstract PURPOSE:To obtain an optimum gain by causing a tunnel robot to learn using a directional control simulation a neural network to which initial deviation and deflection angle are input and from which deviation and deflection angle feedback gains are output. CONSTITUTION:A neural network N is provided to which deviation and deflection angle Y, are input and from which deviation and deflection angle feedback gains Kp, Ka are output. An initial position deviation Y(O) and an initial pitching angle deflection (O) are put in the input layer I of the neural network N and the deviation and deflection angle feedback gains Kp, Ka are output from the output layer O of the network N. These gains are put in a directional control simulation which uses feedback control laws, so that a robot learns the gains. If the difference between the gain which the robot is learning and that which the robot has learned last time is reduced below a predetermined set value as the learning is repeated a fixed number of times, the gains are regarded as having converged and the value of the gain obtained at that time is used as an optimum gain.

Inventors

• 👤 Shinichi Aoshima
• 👤 伸一 青島
• 👤 Koki Takeda
• 👤 武田　幸喜
• 👤 Tetsuo Yabuta
• 👤 藪田　哲郎

Bibliographic Details