NDLI: A study on precise control of industrial robot arm for manufacturing process automation

Content Provider	IEEE Xplore Digital Library
Author	Jun-Seok Yang Young-Mok Koo Moon-Youl Park Hyun-Suk Sim Huu Cong Nguyen Sung-Hyun Han
Copyright Year	2014
Description	Author affiliation: DongSan Tech Co. Ltd., South Korea (Hyun-Suk Sim) \|\| Michelin Vietnam Co. Ltd., Vietnam (Huu Cong Nguyen) \|\| Dept. of Adv. Eng., Kyungnam Univ., Changwon, South Korea (Jun-Seok Yang; Young-Mok Koo; Moon-Youl Park) \|\| Sch. of Mechanmical Eng., Kyungnam Univ., Changwon, South Korea (Sung-Hyun Han)
Abstract	In this paper, we present two kinds of robust control schemes for robot system which has the parametric uncertainties. In order to compensate these uncertainties, we use the neural network control system that has the capability to approximate any nonlinear function over the compact input space. In the proposed control schemes, we need not derive the linear formulation of robot dynamic equation and tune the parameters. We also suggest the robust adaptive control laws in all proposed schemes for decreasing the effect of approximation error. To reduce the number of neural of network, we consider the properties of robot dynamics and the decomposition of the uncertainty function. The proposed controllers are robust not only to the structured uncertainty such as payload parameter, but also to the unstructured one such as friction model and disturbance. The reliability of the control scheme is shown by computer simulations and experiment of robot manipulator with 7 axis.
Starting Page	316
Ending Page	318
File Size	716364
Page Count	3
File Format	PDF
ISSN	20937121
e-ISBN	9788993215076
DOI	10.1109/ICCAS.2014.6988012
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-22
Publisher Place	South Korea
Access Restriction	Subscribed
Rights Holder	Institute of Control, Robotics and Systems (ICROS)
Subject Keyword	Robust control robot dynamics decomposition uncertainty neural network
Content Type	Text
Resource Type	Article

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