Adaptive fuzzy neural control of multiple-link robot manipulators

Abstract

This article presents the design, development, and implementation of a new adaptive fuzzy neural controller (AFNC) suitable for real-time industrial applications. The developed AFNC consists of a combination of a fuzzy neural network (FNN) controller and a supervisory PD controller. The salient features of the AFNC are: (1) dynamic fuzzy neural structure, that is, fuzzy control rules can be generated or deleted automatically; (2) fast on-line learning ability; (3) fast convergence of tracking error; (4) adaptive control; and (5) robust control, where global stability of the system is established using Lyapunov approach. Experimental evaluation conducted on a SEIKO TT-3000 SCARA robot demonstrates that excellent tracking performance can be achieved under time-varying conditions. The proposed controller also outperforms some of the existing adaptive fuzzy and neural controllers in terms of tracking speed and accuracy.