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A Neural Network Based Sliding Mode Control for Tracking Performance with Parameters Variation of a 3-DOF Manipulator

School of Mechanical Engineering, University of Ulsan Daehakro 93, Nam-gu, Ulsan 680-764, Korea
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Appl. Sci. 2019, 9(10), 2023; https://doi.org/10.3390/app9102023
Received: 7 April 2019 / Revised: 8 May 2019 / Accepted: 10 May 2019 / Published: 16 May 2019
(This article belongs to the Section Mechanical Engineering)
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Abstract

The manipulator, in most cases, works in unstructured and changeable conditions. With large external variations, the demand for stability and robustness must be ensured. This paper proposes a neural network sliding mode control (NNSMC) to cope with uncertainties and improve the behavior of the robotic manipulator in the presence of an external disturbance. The proposed method is applied to the three degrees of freedom (DOF) manipulator. Some comparisons between the proposed and the conventional algorithms are given in both simulation and experiments to prove that the designed control can achieve higher accuracy in tracking motion. View Full-Text
Keywords: neural network control; sliding mode control; self-tuning online; manipulator; adaptive control neural network control; sliding mode control; self-tuning online; manipulator; adaptive control
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Truong, H.-V.-A.; Tran, D.-T.; Ahn, K.K. A Neural Network Based Sliding Mode Control for Tracking Performance with Parameters Variation of a 3-DOF Manipulator. Appl. Sci. 2019, 9, 2023.

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