A Variable Structure-Based Estimation Strategy Applied to an RRR Robot System

DOI

10.2991/jrnal.2017.4.2.8How to use a DOI?

Keywords

Robotic manipulator, estimation theory, Kalman filter, smooth variable structure filter.

Abstract

Nonlinear estimation strategies are important for accurate and reliable control of robotic manipulators. This brief 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 robust smooth variable structure filter (SVSF). The EKF is included to present a basic background in estimation techniques and the SVSF is described and implemented on the system. We simulate the SVSF applied to a dynamically modeled three-link robotic manipulator. The results of the paper demonstrate that nonlinear estimation techniques such as the SVSF can be applied effectively to robots with modeling uncertainty and external disturbances.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Jacob Goodman
AU  - Jinho Kim
AU  - Andrew S. Lee
AU  - S. Andrew Gadsden
AU  - Mohammad Al-Shabi
PY  - 2017
DA  - 2017/09/01
TI  - A Variable Structure-Based Estimation Strategy Applied to an RRR Robot System
JO  - Journal of Robotics, Networking and Artificial Life
SP  - 142
EP  - 145
VL  - 4
IS  - 2
SN  - 2352-6386
UR  - https://doi.org/10.2991/jrnal.2017.4.2.8
DO  - 10.2991/jrnal.2017.4.2.8
ID  - Goodman2017
ER  -