Real-time dynamic control of an industrial manipulator using a neural network-based learning controller

A learning control technique that uses an extension of the cerebellar model articulation control network developed by J.S. Albus (1975) is discussed, and results of real-time control experiments that involved learning the dynamics of a five-axis industrial robot (General Electric P-5) during high-speed 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 trials, and to be relatively insensitive to the choice of control system gains. The effects of network memory size and trajectory characteristics on learning system performance were investigated