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Adaptive control algorithm of flexible robotic gripper by extreme learning machine

Title
Adaptive control algorithm of flexible robotic gripper by extreme learning machine
Author
Zal Nezhad, Erfan
Keywords
Flexible gripper; Sensors; Object detection; Soft computing
Issue Date
2016-02
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Citation
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING, v. 37, Page. 170-178
Abstract
Adaptive grippers should be able to detect and recognize grasping objects. To be able to do it control algorithm need to be established to control gripper tasks. Since the gripper movements are highly nonlinear systems it is desirable to avoid using of conventional control strategies for robotic manipulators. Instead of the conventional control strategies more advances algorithms can be used. In this study several soft computing methods are analyzed for robotic gripper applications. The gripper structure is fully compliant with embedded sensors. The sensors could be used for grasping shape detection. As soft computing methods, extreme learning machine (ELM) and support vector regression (SVR) were established. Also other soft computing methods are analyzed like fuzzy, neuro-fuzzy and artificial neural network approach. The results show the highest accuracy with ELM approach than other soft computing methods. (C) 2015 Elsevier Ltd. All rights reserved.
URI
http://www.sciencedirect.com/science/article/pii/S0736584515000915http://hdl.handle.net/20.500.11754/31785
ISSN
0736-5845; 1879-2537
DOI
10.1016/j.rcim.2015.09.006
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > MECHANICAL ENGINEERING(기계공학부) > Articles
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