Robust friction state observer and recurrent fuzzy neural network design for dynamic friction compensation with backstepping control
In this article, we investigate a robust friction compensation scheme for the purpose of accomplishing high-precision positioning performance in a servo mechanical system with nonlinear dynamic friction. To estimate the friction state and tackle the robustness problem for uncertainty, a recurrent fuzzy neural network (RFNN) and reconstructed error compensator as well as a robust friction state observer are developed. The asymptotic stability of the series of friction compensation methodologies are verified from the Lyapunov's stability theory. Some simulations and experiments on a frictional servo mechanical system were carried out to evaluate the effectiveness of the proposed control scheme. (C) 2010 Elsevier Ltd. All rights reserved.
robust friction state observer recurrent fuzzy neural networks backstepping control servo mechanical system reconstructed error compensator lugre friction model