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The Unscented Smooth Variable Structure Filter Application Into a Robotic Arm

[+] Author Affiliations
Mohammad Al-Shabi

Philadelphia University, Jerash, Jordan

Khaled S. Hatamleh

Jordan University of Science & Technology, Irbid, Jordan

Paper No. IMECE2014-40118, pp. V04BT04A037; 10 pages
doi:10.1115/IMECE2014-40118
From:
  • ASME 2014 International Mechanical Engineering Congress and Exposition
  • Volume 4B: Dynamics, Vibration, and Control
  • Montreal, Quebec, Canada, November 14–20, 2014
  • Conference Sponsors: ASME
  • ISBN: 978-0-7918-4648-3
  • Copyright © 2014 by ASME

abstract

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.

Copyright © 2014 by ASME
Topics: Robotics , Filters

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