Neuro-fuzzy minimum torque change control of DD manipulator

Abstract

A minimum torque-change model of a robotic manipulator was proposed by Uno et al. (1989), in which a function of torque change is minimized. The objective function depends on the nonlinear dynamics of the manipulator. A trajectory with best performance was obtained by the iterative scheme using the method of variational calculus and dynamic optimization theory. Though the method is computationally economical, it seems to be a control theoretic approach rather than a neuro scientific one. In this paper, the authors propose a direct solution method of this variational problem using Gaussian radial basis functions. The function can be regarded as both a three layered neural network (Moody and Darken, 1989) and a simplified fuzzy reasoning model.