GS9-5 Nonlinear Estimation Strategies Applied on an RRR Robotic Manipulator

Title:	GS9-5 Nonlinear Estimation Strategies Applied on an RRR Robotic Manipulator
Publication:	ICAROB2017
Volume:	22
Pages:	342-345
ISSN:	2188-7829
DOI:	10.5954/ICAROB.2017.GS9-5
Author(s):	Jacob Goodman, Jinho Kim, Andrew S. Lee, S. Andrew Gadsden, Mohammad Al-Shabi
Publication Date:	January 19, 2017
Keywords:	Estimation theory, Kalman filter, smooth variable structure filter, robotic manipulator
Abstract:	Nonlinear estimation strategies are important for accurate and reliable control of robotic manipulators. This paper studies the application of estimation theory to a simple robotic manipulator. Two estimation techniques are considered; the classic extended Kalman filter (EKF), and the smooth variable structure filter (SVSF). The EKF is included to present a basic background in estimation techniques and the SVSF demonstrates an example of the stateof-the art. We simulate the SVSF applied to a dynamically modeled three-link (RRR) robotic manipulator. The results of the paper demonstrate that nonlinear estimation techniques such as the SVSF can be applied effectively. Suggestions for future estimation and robotics research are also provided.
PDF File:	https://alife-robotics.co.jp/members2017/icarob/data/html/data/GS_pdf/GS9/GS9-5.pdf
Copyright:	© The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/

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