Inverse kinematic neuro-control of robotic systems

Abstract

The emergence of the theory of dynamic neural computing has made it possible to develop neural learning and adaptive schemes that can be used to obtain feasible solutions to complex control problems, such as inverse kinematic control for robotic systems. In this paper, such a neural learning scheme using a multilayered dynamic neural network (MDNN) is proposed. The basic dynamic computing element of MDNN is a dynamic neural unit (DNU) developed in this paper. The learning and adaptive capabilities of DNU can be used for developing complex dynamic structures. In this paper, we have used DNU for developing a MDNN for the inverse kinematic control of a two-link robot. The validity of the proposed scheme is demonstrated through computer simulation studies.