Multiple Fuzzy Control of a Robot Manipulator.

Abstract

A learning fuzzy controller is described for controlling a robot manipulator. The learning controller used here is called a multiple fuzzy controller, in which several elemental fuzzy controllers are processed in parallel and the degree of usage of each inferred consequent is determined by using a linear neural network. Two learning algorithms are considered in the framework of the specialized learning architecture : one is based on minimizing the squared sum of the trajectory error for each link and the other is based on directly minimizing the trajectory squared error for each link. The effectiveness of the proposed controller is illustrated by making some simulations for a two-link manipulator.