Neural network application for robotic motion control-adaptation and learning

Abstract

The authors consider neural network applications to robotic motion control in which the controller is used for the position and force control of robotic manipulators. The proposed neural servo controller is based on a neural network consisting of two hidden layers and input/output layers. The controller can adjust the neural network output to the robot in the forward manner to cooperate with the feedback loop, depending on unknown characteristics of handling objects. In particular, the proposed neural network has delay elements in itself, so that it can learn the dynamics of the system. Simulations are carried out for the case of one- and two-dimensional robotic manipulators. The performance of the proposed neural servo controller is shown in terms of its frequency response, and the robustness against impulsive noises is also shown. The authors propose a fuzzy turbo to avoid stagnation, so that the neural network can learn the dynamical system quickly