Abstract
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 Kp and the angular deviation multiplied by feedback gain Ka. In this paper, we use a neural network to obtain feedback gains Kp and Ka. The input of the neural network is the initial deviation and initial angular deviation. The output of the neural network is the feedback gains Kp and Ka. 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.
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