S. Aoshima et al., DYNAMIC SIMPLIFIED MODEL AND AUTOTUNING OF FEEDBACK GAIN FOR DIRECTIONAL CONTROL USING A NEURAL-NETWORK FOR A SMALL TUNNELING ROBOT, JSME international journal. Series C, dynamics, control, robotics, design and manufacturing, 40(2), 1997, pp. 245-252
This paper describes a simplified dynamic model and autotuning of feed
back gain for the directional control of a small tunneling robot. Firs
t, we constructed a dynamic model for the amount of directional correc
tion and determined its parameters by the least squares method. Next,
we used a neural network to automatically obtain four feedback gains f
or the directional control of both pitching and yawing. The inputs for
the neural network are an initial deviation and an initial angular de
viation for pitching and yawing. The outputs of the neural network are
the feedback gains for angular deviations and deviations. The neural
network learns from the deviations obtained in the simulations. The ne
ural network, which can adapt to any initial deviations, was formed us
ing plural initial deviations in learning. Moreover, this method can t
une the optimum gain for any design line. These results establish the
validity of the proposed method.