Design of control systems using quaternion neural network and its application to inverse kinematics of robot manipulator

Abstract

In this paper, multi-layer quaternion neural networks that conduct their learning by using quaternion back-propagation algorithm are applied to inverse kinematics control of a 2-link robot manipulator as the first step of utilizing the quaternion neural network for control applications. Three architectures of control system using the quaternion neural network, general learning, specialized learning and on-line specialized learning, are presented and their characteristics are investigated. The experimental results show that in apposite architectures, the learning of quaternion neural network converges with a fewer number of iterations compared with the conventional neural network which has more complex network topology and more parameters in real number being employed.