Non-linear friction compensation using backstepping control and robust friction state observer with recurrent fuzzy neural networks

Abstract

A robust precise tracking control for a servomechanical system with non-linear dynamic friction is presented. The LuGre friction that is adopted as a non-linear friction model contains both a directly immeasurable friction state variable and the uncertainty caused by incomplete parameter identification and change of the condition of contact surface. To provide an efficient solution to these problems, a composite robust control scheme is proposed, which consists of a robust friction state observer, a recurrent fuzzy neural network (RFNN) approximator, and an adaptive reconstructed error compensator with backstepping control. A robust friction state observer is designed to estimate the unknown internal state of the LuGre friction model. Next, a proposed RFNN scheme approximates the lumped friction torque uncertainty. Finally, an adaptive error compensator eliminates a reconstructed error arising from RFNN approximation. Some simulations and experiments for a ball-screw servosystem are carried out to demonstrate the performance of a proposed control scheme.

Keywords

LuGre friction model backstepping control robust friction state observer recurrent fuzzy neural networks adaptive reconstructed error compensator servo mechanical system

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