Real time experiments in neural network based learning control during high speed nonrepetitive robotic operations

Abstract

A learning control technique which uses an extension of the CMAC (cerebellar model articulation controller) network developed by J.S. Albus is discussed, and the results of real-time control experiments which involved learning the dynamics of a five-axis industrial robot during high-speed, nonrepetitive movements are presented. During each control cycle, a training scheme was used to adjust the weights in the network in order to form an approximate dynamic model of the robot in appropriate regions of the control space. Simultaneously, the network was used during each control cycle to predict the actuator drives required to follow a desired trajectory, and these drives were used as feedforward terms in parallel to a fixed gain linear feedback controller. Trajectory tracking errors were found to converge to low values within a few training trails for both repetitive and nonrepetitive operations.<>