Neural network control of a pneumatic robot arm

Abstract
A neural map algorithm has been employed to control a five-joint pneu- matic robot arm and gripper through feedback from two video cameras. The pneumatically driven robot arm,(SoftArm) employed,in this inves- tigation shares essential mechanical characteristics with skeletal muscle systems. To control the position of the arm, 200 neurons formed a net- work representing the three-dimensional workspace embedded,in a four- dimensional system of coordinates from the two cameras, and learned a three-dimensional set of pressures corresponding to the end eector,posi- tions, as well as a set of 3◊4 Jacobian matrices for interpolating between these positions. The gripper orientation was achieved through adaptation of a 1 ◊ 4 Jacobian matrix for a fourth joint. Because of the properties of the rubber-tube actuators of the SoftArm, the position as a function of supplied pressure is nonlinear, nonseparable, and exhibits hysteresis. Nevertheless, through the neural network learning algorithm the position could be controlled to an accuracy of about one pixel ( 3 mm) after two hundred,learning steps and the orientation could be controlled to two pixels after eight hundred learning steps. This was achieved through employment,of a linear correction algorithm using the Jacobian matrices mentioned above. Applications of repeated corrections in each position-