Practical Demonstration Of A Learning Control System For A Five-Axis Industrial Robot

摘要

The overall complexity of many robotic control problems, and the ideal of a truly general robotic control system, have led to much discussion of the use of neural networks in robot control. This paper discusses a learning control technique which uses an extension of the CMAC network developed by Albus, and presents the results of real time control experiments which involved learning the dynamics of a 5 axis industrial robot (General Electric P-5) during high speed movements. 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 trials, and to be relatively insensitive to the choice of feedback control system gains.