A neural-network approach to robotic control

Abstract

An artificial neural-network paradigm for the control of robotic systems is presented. The approach is based on the Cerebellar Model Articulation Controller created by James Albus and incorporates several extensions. First, recognizing the essential structure of multibody equations of motion, two parallel modules are used that directly reflect the dynamical characteristics of multibody systems. Second, the architecture of the proposed network is imbued with a self-organizational capability which improves efficiency and accuracy. Also, the networks can be arranged in hierarchical fashion with each subsequent network providing finer and finer resolution.