Adaptive dynamic friction observer and recurrent fuzzy neural network estimator design with backstepping control

Abstract

In this study, robust non-linear dynamic friction control is considered using a dynamic friction observer and intelligent control. An adaptive dynamic friction observer based on the LuGre friction model is proposed to estimate the friction parameters and a directly immeasurable friction state variable. A recurrent fuzzy neural network (RFNN) approximator and reconstructed error compensator are also designed to give additional robustness to the control system under friction model uncertainty. A proposed composite control scheme with a basic backstepping controller is applied to the position tracking control of the servo system. The performances of the proposed friction observer and the friction controller are demonstrated by some simulations and experiments.