Robotic arms are becoming increasingly popular in industrial applications. However, improving the response and accuracy of robotic arms while reducing their cost has become challenging. The Kalman Filter (KF) has attracted a significant amount of research as it improves the control quality by filtering the feedback signal. On the other hand, KF solution becomes very challenging when the system under study is nonlinear. This work proposes a new online state estimation algorithm that combines the Smooth Variable Structure Filter (SVSF) with the Unscented Kalman Filter (UKF). The proposed method overcomes the limitations of SVSF and UKF in terms of stability and sensitivity to noise. A simulation study is conducted in this paper to demonstrate the results of the proposed method when applied to estimate the states of a PRRR industrial robotic arm.
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ASME 2014 International Mechanical Engineering Congress and Exposition
November 14–20, 2014
Montreal, Quebec, Canada
Conference Sponsors:
- ASME
ISBN:
978-0-7918-4648-3
PROCEEDINGS PAPER
The Unscented Smooth Variable Structure Filter Application Into a Robotic Arm
Mohammad Al-Shabi,
Mohammad Al-Shabi
Philadelphia University, Jerash, Jordan
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Khaled S. Hatamleh
Khaled S. Hatamleh
Jordan University of Science & Technology, Irbid, Jordan
Search for other works by this author on:
Mohammad Al-Shabi
Philadelphia University, Jerash, Jordan
Khaled S. Hatamleh
Jordan University of Science & Technology, Irbid, Jordan
Paper No:
IMECE2014-40118, V04BT04A037; 10 pages
Published Online:
March 13, 2015
Citation
Al-Shabi, M, & Hatamleh, KS. "The Unscented Smooth Variable Structure Filter Application Into a Robotic Arm." Proceedings of the ASME 2014 International Mechanical Engineering Congress and Exposition. Volume 4B: Dynamics, Vibration, and Control. Montreal, Quebec, Canada. November 14–20, 2014. V04BT04A037. ASME. https://doi.org/10.1115/IMECE2014-40118
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