Robust friction state observer and recurrent fuzzy neural network design for dynamic friction compensation with backstepping control

被引：42

作者：

Han, Seong Ik ^{[1
]}

Lee, Kwon Soon ^{[1
]}

机构：

[1] Dong A Univ, Dept Elect Engn, Pusan, South Korea

来源：

MECHATRONICS | 2010年 / 20卷 / 03期

关键词：

LuGre friction model; Backstepping control; Robust friction state observer; Recurrent fuzzy neural networks; Reconstructed error compensator; Servo mechanical system; MOTOR DRIVE; MODEL; SERVO; SYSTEMS;

D O I：

10.1016/j.mechatronics.2010.02.005

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：384 / 401

页数：18

共 28 条

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