Defining a neural network controller structure for a rubbertuator robot.

Abstract

Rubbertuator (Rubber-Actuator) robot arm is a pneumatic robot, unique with its lightweight, high power, compliant and spark free nature. Compressibility of air in the actuator tubes and the elastic nature of the rubber, however, are the two major sources of increased non-linearity and complexity in motion control. Soft computing, exploiting the tolerance of uncertainty and vagueness in cognitive reasoning has been offering easy to handle, robust, and low-priced solutions to several non-linear industrial applications. Nonetheless, the black-box approach in these systems results in application specific architectures with some important design parameters left for fine tuning (i.e. number of nodes in a neural network). In this study we propose a more systematic method in defining the structure of a soft computing technique, namely the backpropagation neural network, when used as a controller for rubbertuator robot systems. The structure of the neural network is based on the physical model of the robot, while the neural network itself is trained to learn the trajectory independent parameters of the model that are essential for defining the robot dynamics. The proposed system performance was compared with a well-tuned PID controller and shown to be more accurate in trajectory control for rubbertuator robots.