ON-LINE LEARNING OF ROBOT INVERSE DYNAMICS WITH CEREBELLAR MODEL CONTROLLER IN FEEDFORWARD CONFIGURATION

Performance of robot control in trajectory tracking can be improved considerably if robot inverse dynamics model is known. It may be used in feedforward or in computed torque configuration. Cerebellar model controllers can be used to acquire inverse robot dynamics model on-line. In this paper we explore different structural aspects of cerebellar controller in feedforward configuration for improving robot control performance. Cerebellar controller is used beside conventional proportionalderivative controller, and it learns by using output of later as teaching signal. Effects of cerebellar controller with dimensionality of input space lower than that of the problem to be learned is explored. Fully coupled Albus overlays with uniform population coding for input dimensions, at different number, shape and width of receptive fields, in accuracy of acquired model is investigated. Root-mean-square of position and speed error is used as measure of control performance. How normalization of receptive fields affects cerebellar control performance is explored by using receptive fields with self-normalization property and those without it. Simulink model of cerebellar controller that preserves layered organization is used, along robot plant model built in SimMechanics.