Volume 4, Issue 2, September 2017, Pages 142 - 145
A Variable Structure-Based Estimation Strategy Applied to an RRR Robot System
Jacob Goodman, Jinho Kim, Andrew S. Lee, S. Andrew Gadsden, Mohammad Al-Shabi
Available Online 1 September 2017.
- https://doi.org/10.2991/jrnal.2017.4.2.8How to use a DOI?
- Robotic manipulator, estimation theory, Kalman filter, smooth variable structure filter.
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 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 - https://doi.org/10.2991/jrnal.2017.4.2.8 ID - Goodman2017 ER -