Auto-tuning of feedback gains using a neural network for a small tunnelling robot

Describes the auto-tuning of feedback gains for a small tunnelling robot. The authors (1989) have already proposed the directional control method 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, they use a neural network to obtain feedback gains Kp and Ka. The inputs of the neural network are an initial deviation and an initial angular deviation. The outputs of the neural network are the feedback gains Kp and Ka. This neural network learns from the deviation errors. 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 deviations were formed by using plural initial deviations in learning. Moreover, this method can tune the optimum gains to any design line. The results showed the validity of the proposed auto-tuning method