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Disturbance estimation for robotic systems using continuous integral sliding mode observer

delete2022-06-29
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PRE
AI
Z
Zengjie Zhang
D
Dirk Wollherr
H
Homayoun Najjaran *
DOI:10.1002/rnc.6252delete
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摘要

摘要

En 中文
This article presents a novel force-sensor-less method for the estimation of external forces for a general class of second-order robotic systems. The method is based on the integral sliding mode observer (ISMO) which serves as a second-order differentiator for the position measurement of the system. As a result, the system states and disturbance are estimated without explicitly using force and velocity measurements. To apply the ISMO to the general second-order systems, a proper assumption is proposed to address their nonlinearity and discontinuity. The boundary-layer method is applied to ensure that the virtual inputs of the observer are continuous such that the chattering phenomenon is attenuated. A Lyapunov-based method is used to analyze the influence of the boundary layers on the convergence of the state- and disturbance-estimation errors. This influence, mainly determined by the boundary-layer scalars, is given in analytical forms as a reference for parameter selection. The method is evaluated by numerical simulation on a robot manipulator system and compared with a conventional sliding mode observer (SMO). The validation of the performance of the continuous ISMO indicates its generalizability to general second-order robotic systems. Also, the advantage of continuous ISMO over the conventional SMO is reflected by its small estimation errors and superior responsiveness. In general, the proposed method in this paper may interest those who are seeking solutions for haptic robotic tasks without using force sensors.
Keyword:
disturbance observation
fault detection and isolation
integral sliding mode
robotic system
sliding mode observer

期刊

International Journal of Robust and Nonlinear Control 封面图
International Journal of Robust and Nonlinear Control
IF:
3.2
论文数:
7.0K
被引数:
1.4W

机构

U
University of Victoria
学者数:
1.0W
论文数: 1.0W
被引数: 1.5W
T
Technical University of Munich
学者数:
5.2W
论文数: 3.9W
被引数: 6.2W
U
University of British Columbia
学者数:
7.0W
论文数: 6.1W
被引数: 8.6W
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