Neural Compensation Technique for Fuzzy Controlled Humanoid Robot Arms : Experimental Studies

Abstract

In this paper, a neural network compensation technique is proposed for a fuzzy controlled humanoid robot arm. The robot arm is controlled by fuzzy controllers, and then neural network controller is added to improve the performance for system variations by modifying fuzzy rules. The overall structure forms a neuro-fuzzy controlled system, in the sense that the proposed control algorithm can have the effect of changing fuzzy rules. Experimental studies have been carried out to test the performance of the proposed control algorithm. Experimental results have confirmed that the proposed neural network compensation scheme for fuzzy controlled systems works best among several control methods.