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• W2062022268 abstract "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." @default.
• W2062022268 created "2016-06-24" @default.
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• W2062022268 date "2010-04-01" @default.
• W2062022268 modified "2023-09-27" @default.
• W2062022268 title "Robust friction state observer and recurrent fuzzy neural network design for dynamic friction compensation with backstepping control" @default.
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• W2062022268 doi "https://doi.org/10.1016/j.mechatronics.2010.02.005" @default.
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