Control of Robot Manipulator with Uncertain Dynamics by Adaptive Fuzzy Controller

Abstract

To exploit the beneficial features of feedback linearization control and fuzzy control, and also to overcome their disadvantages, this paper presents a robust adaptive fuzzy control scheme combining conventional feedback linearization control and a compensator for the robust tracking control of robotic manipulators with uncertainties in forms of structured and unstructured. The proposed compensator is based on adaptive fuzzy estimation and compensation of uncertainty. The adaptive fuzzy system compensates uncertainties by modeling of the uncertainties as a nonlinear function of the joint position variables. The novelty and advantage of the proposed adaptive fuzzy system is to use a non-complicated structure and without applying all system states for estimating the uncertainty, so the number of fuzzy rules is reduced. According to Lyapunov stability theory, a tracking error limit is derived for the closed‐ loop control system and accordingly the convergence and stability of the control scheme is proved. A comparison between the proposed adaptive fuzzy control and conventional feedback linearization controller at presence of uncertainties is presented. The authenticity of the proposed control scheme is showcased by numerical simulations of a SCARA robot manipulator.